DE102012103552A1 - AUDIO SYSTEM AND METHOD FOR USING ADAPTIVE INTELLIGENCE TO DISTINCT THE INFORMATION CONTENT OF AUDIO SIGNALS AND TO CONTROL A SIGNAL PROCESSING FUNCTION - Google Patents

Abstract

An audio system has a signal processor coupled to receive an audio signal from a musical instrument or vocals. A time domain processor receives the audio signal and generates time domain parameters of the audio signal. A frequency domain processor receives the audio signal and generates frequency domain parameters of the audio signal. The audio signal is sampled and the time domain processor and frequency domain processor process a plurality of frames of the sampled audio signal. The time domain processor detects the start of a note of the sampled audio signal. A signature database has signature data records, each with time domain parameters and frequency domain parameters and control parameters. A detection detector compares the time domain parameters and frequency domain parameters of the audio signal with a signature data record of the signature database. The control parameters of the matching signature.

Description

Field of the invention

The present invention relates generally to audio systems and, more particularly, to an audio system and method for using adaptive intelligence to distinguish the dynamic content of an audio signal generated by a musical instrument and to control a signal processing function associated with the audio signal.

Background of the invention

Audio sound systems are commonly used to amplify signals and reproduce audible sound. A sound generation source, such as. As a musical instrument, a microphone, a multimedia player or other electronic device, generates an electrical audio signal. The audio signal is routed to an audio amplifier, which controls the amplitude and performs another signal processing on the audio signal. The audio amplifier may perform filtering, modulation, distortion enhancement or reduction, sound effects, and other signal processing functions to improve the audio quality and frequency characteristics of the audio signal. The amplified audio signal is sent to a speaker to convert the electrical signal into audible sound and to reproduce the sound generating source with enhancements introduced by the signal processing function.

Musical instruments have always been very popular in society, providing entertainment, social interaction, self-expression, and a business and livelihood for many people. Stringed instruments are particularly popular because of their active playability, sound characteristics and portability. Stringed instruments are entertaining yet challenging to play, have great sound qualities and are easy to move from one place to another.

In one example, the sound generation source may be an electric guitar or electric bass guitar, which is a well-known musical instrument. The guitar has an audio output that connects to an audio amplifier. The output of the audio amplifier is connected to a speaker to produce audible music sounds. In some cases, the audio amplifier and speaker are separate units. In other systems, the units are integrated into a portable housing.

The electric guitar typically requires an audio amplifier for it to work. Other guitars use the amp to enhance the sound. The guitar audio amplifier creates features such as. Amplification, filtering, tone equalization and sound effects. The user adjusts the knobs on the front panel of the audio amplifier to select the desired volume, acoustics and sound effects.

However, most if not all audio amplifiers are limited in the features they can each provide. High end amplifiers provide more high quality sound reproduction and a variety of signal processing options, but are generally expensive and difficult to transport. The speaker in the high quality equipment is typically a separate unit from the amplifier. A cheap amplifier can be more affordable and portable, but has limited sound enhancement features. There are few amplifiers for the low to medium consumer market that provide full features, easy portability and low cost.

In audio playback, it is common to use a variety of signal processing techniques depending on the music and style of play to achieve better sound quality, playability, and otherwise enhance the creativity of the artist as well as enjoyment and recognition of the composition by the listener. For example, guitar players use a wide variety of audio amplifier settings and sound effects for different styles of music. Bass players use different compressors and equalization settings to improve the sound quality. Singers use different reverb and equalization settings depending on the lyrics and melody of the song. Music producers use post-processing effects to improve the composition. For home and car sound systems, the user can choose different reverb and equalization presets to optimize the performance of classical or rock music.

Audio amplifiers and other signal processing equipment, e.g. As a dedicated amplifier, a pedal keyboard or a soundtrack, are typically front panel switches and - Controlled control buttons. To accommodate the processing requirements for different styles of music, the user hears the desired functions, e.g. Amplification, filtering, tone equalization, and sound effects, and selects them manually by adjusting the switch positions and rotating the control knobs. If playing styles change or change to a different tune, the user must temporarily pause the playback to make adjustments to the audio amplifier or other signal processing equipment. In some digital or analog instruments, the user can configure and save preferred settings as defaults and then later manually select the stored settings or factory default settings for the instrument.

In professional applications, a technician can make adjustments to the audio amplifier or other signal processing equipment while the artist performs, but the synchronization between the artist and the technician is usually less than ideal. If the artist changes the attack on the strings or the vocal part or starts a new composition, the technician has to anticipate the act of the artist and make manual adjustments to the audio amplifier accordingly. In most, if not all cases, the audio amplifier is rarely optimized for the musical sounds, at least not on a note-by-note basis.

Summary of the invention

There is a need for dynamically controlling an audio amplifier or other signal processing equipment in real time. Thus, in one embodiment, the present invention is an audio system having a signal processor coupled to receive an audio signal. The dynamic content of the audio signal controls the operation of the signal processor.

In another embodiment, the present invention is a method of controlling an audio system comprising the steps of providing a signal processor configured to receive an audio signal and controlling the operation of the signal processor using the dynamic content of the audio signal.

In another embodiment, the present invention is an audio system having a signal processor coupled to receive an audio signal. A time domain processor receives the audio signal and generates time domain parameters of the audio signal. A frequency domain processor receives the audio signal and generates frequency domain parameters of the audio signal. A signature database comprises a plurality of signature data records, each having time domain parameters and frequency domain parameters and control parameters. A recognition detector compares the time domain parameters and frequency domain parameters of the audio signal with a signature data set of the signature database. The control parameters of the matching signature data set control the operation of the signal processor.

In another embodiment, the present invention is a method of controlling an audio system comprising the steps of providing a signal processor adapted to receive an audio signal, generating time domain parameters of the audio signal, generating frequency domain parameters of the audio signal, providing a signature database with a A plurality of signature data sets each having time domain parameters and frequency domain parameters and control parameters, comparing the time domain parameters and the frequency domain parameters of the audio signal with a signature database of the signature database and controlling the operation of the signal processor based on the control parameters of the matching signature data set.

Brief description of the drawings

1 represents an audio sound source which generates an audio signal and passes the audio signal to a speaker through signal processing equipment;

2 Fig. 10 illustrates a guitar connected to an audio sound system;

3 illustrates a front view of the audio system housing with a front panel;

4 represents another detail of the front panel of the audio system;

5 illustrates an audio amplifier and speaker in separate housings;

6 FIG. 12 is a block diagram of the adaptive intelligence control audio amplifier; FIG.

7a - 7b represent waveform diagrams of the audio signal;

8th Fig. 12 is a block diagram of the frequency domain and time domain analysis block;

9a - 9b represent time sequence frames of the sampled audio signal;

10 Fig. 12 is a block diagram of the time domain analysis block;

11 FIG. 12 illustrates a block diagram of the time domain energy level isolation block in frequency bands; FIG.

12 Fig. 12 is a block diagram of the time domain not detector block;

13 Fig. 12 is a block diagram of the time domain insert detector;

14 illustrates another embodiment of the time domain insert detector;

15 Fig. 10 is a block diagram of the frequency domain analysis block;

16 Fig. 10 is a block diagram of the frequency domain not detector block;

17 FIG. 12 is a block diagram of energy level isolation in frequency interval ranges; FIG.

18 Fig. 10 is a block diagram of the frequency domain insert detector;

19 illustrates another embodiment of the frequency domain insert detector;

20 represents the note signature database with parameter values, weighting values and control parameters;

21 represents a computer interface to the note signature database;

22 represents a recognition detector for the runtime matrix and the note signature database;

23 FIG. 12 illustrates an embodiment wherein the adaptive intelligence controller is implemented with separate signal processing equipment, a separate audio amplifier, and a separate speaker;

24 represents the signal processing equipment implemented as a computer;

25 Fig. 12 is a block diagram of the signal processing function within the computer;

26 represents the signal processing equipment implemented as a pedal keyboard;

27 depicts the signal processing equipment implemented as a signal processor housing;

28 represents a vocal sound source which is routed to an audio amplifier and a speaker;

29 FIG. 12 illustrates a block diagram of the adaptive intelligence control audio amplifier on a frame by frame basis; FIG.

30 FIG. 12 illustrates a block diagram of the frequency domain and time domain analysis block on a frame by frame basis; FIG.

31a - 31b represent time sequence frames of the sampled audio signal;

32 Fig. 12 is a block diagram of the time domain analysis block;

33 FIG. 12 illustrates a block diagram of the time domain energy level isolation block in frequency bands; FIG.

34 Fig. 10 is a block diagram of the frequency domain analysis block;

35 represents the frame signature database with a parameter value, weighting values and control parameters;

36 represents a computer interface to the frame signature database; and

37 represents a recognition detector for the runtime matrix and the frame signature database.

Detailed description of the drawings

The present invention will be described in one or more embodiments in the following description with reference to the figures in which like numerals represent the same or similar elements. Although the invention is described in terms of the best mode for achieving the objects of the invention, it will be apparent to those skilled in the art that it is intended to cover alternatives, modifications and equivalents which may be included within the spirit and scope of the invention as by defines the appended claims and their equivalents as supported by the following disclosure and drawings.

Regarding 1 includes an audio sound system 10 an audio sound source 12 which generates electrical signals that represent sound content. The audio sound source 12 may be a musical instrument, an audio microphone, a multimedia player, or other device capable of producing electrical signals that represent sound content. The musical instrument may be an electric guitar, a bass guitar, a violin, a horn, a brass instrument, drums, a wind instrument, a string instrument, a piano, an electric keyboard, and drums, to name but a few. The electrical signals from the audio sound source 12 be through an audio cable 14 to a signal processing equipment 16 directed to signal conditioning and power amplification. The signal processing equipment 16 may be an audio amplifier, a computer, a pedal keyboard, a signal processing cabinet, or other equipment capable of performing signal processing functions on the audio signal. The signal processing function may include amplification, filtering, equalization, sound effects, and user-defined modules that adjust the power level and enhance the signal characteristics of the audio signal. The signal-processed audio signal is through an audio cable 17 to a speaker 18 directed to the sound content of the audio sound source 12 with those through the signal processing equipment 16 to reproduce the improvements introduced in the audio signal.

2 shows a musical instrument as an audio sound source 12 , in this case an electric guitar 20 , One or more pickups 22 are under the strings 24 the electric guitar 20 and convert the string movement or vibration into electrical signals representing the intended sounds from the vibrating strings. The electrical signals from the guitar 20 be through an audio cable 26 to an audio input jack on the front panel 30 of the audio system 32 directed for signal processing and power amplification. The audio system 32 includes an audio amplifier and speakers that work together inside the case 34 are arranged. The signal conditioning provided by the audio amplifier may include amplification, filtering, equalization, sound effects, user defined modules, and other signal processing functions that adjust the power level and improve the signal characteristics of the audio signal. The signal-processed audio is within the audio system 32 directed to the speaker. The power gain increases or decreases the power level and signal strength of the audio signal to drive the speaker and the sound content produced by the vibrating strings 24 the electric guitar 20 is intended to play with the improvements introduced by the audio amplifier into the audio signal. The front panel 30 includes a display and control buttons to allow the user to adjust various settings of the audio system 32 to monitor and manually control.

3 shows a front view of the audio system 32 , As an initial observation, the form factor and the footprint of the audio system 32 designed for portable use and easy transport. The audio system 32 measures about 13 inches in height, 15 inches in width, and 7 inches in depth, and weighs about 16 pounds. A carrying handle or strap 40 is intended to support the characteristics of portability and ease of transport. The audio system 32 has a housing 42 by a folded aluminum frame, a wooden cabinet, a black vinyl cover, a front panel and a fabric tension over the speaker area 44 is defined. The front panel 30 has audio input connections, headphones, control buttons and buttons, liquid crystal display (LCD) and digital musical instrument interface (MIDI) input / output (I / O) jacks:
Another detail of the front panel 30 of the audio system 32 is in 4 shown. The external features of the audio system 32 include an audio input jack 50 to record the audio cable 26 from the guitar 20 or other musical instruments, a headphone jack 52 For connecting to external headphones, a programmable control panel 54 , Control buttons 56 and MIDI I / O jacks 58 , The control buttons 56 are in addition to the programmable control panel 54 intended for audio control functions that are frequently accessed by the user. In one embodiment, the control buttons see 56 a user control of volume and sound. Additional control buttons 56 can control the frequency response, equalization and other sound control functions.

The programmable control panel 54 includes an LCD 60 , Function mode keys 62 , Selection keys 64 and a knob or dial 66 , The function mode keys 62 and selection buttons 64 are elastomeric rubber blocks for soft touch and long life. Alternatively, the keys may be hard plastic with microelectronic switches with tactical feedback. The audio system 32 is fully programmable, menu driven, and uses software to configure and control the sound reproduction features. The combination of the function mode keys 62 , the selection buttons 64 and the data wheel 66 provides user interface control over the various modes of operation, access to menus for selecting and editing functions, and configuration of the audio system 32 in front. The programmable control panel 54 of the audio system 32 can also include LEDs as indicators for sync / tapping, tempo, storage, recording and performance features.

In general, the programmable control panel 54 the user interface for fully programmable, menu driven configuration and control of electrical functions within the audio system 32 , The LCD 60 changes with user selections to provide many different configuration and operation menus and options. The modes may include startup and self-test, playback, edit, utility, save, and tuner. In one operating mode, the LCD shows 60 the playback mode of the audio system 32 , In another mode, the LCD will show 60 the MIDI data transfer in the process. In another mode, the LCD will show 60 a default setting and preferences. In yet another mode, the LCD shows 60 a voting knife.

If you 5 The audio system can also use an audio amplifier inside a first housing 70 is included, and a speaker inside a second separate housing 72 is implemented. In this case, the audio cable is 26 from the guitar 20 to the audio input jack 74 which are connected to the audio amplifier inside the case 70 for power amplification and signal processing. control buttons 76 at the front panel 78 of the housing 70 allow the user to monitor and manually control various settings of the audio amplifier. The housing 70 is through the audio cable 80 with the housing 72 electrically connected to the amplified and conditioned audio signal to the speakers 82 to lead.

In audio playback, it is common to use a variety of signal processing techniques depending on the content of the audio source, e.g. Performance or style of play to achieve better sound quality, playability, and otherwise enhance the artist's creativity and enjoyment and appreciation of the composition by the listener. For example, bass players use different compressors and equalization settings to improve the sound quality. Singers use different reverb and equalization settings depending on the lyrics and melody of the song. Music producers use post-processing effects to improve the composition. For home and car sound systems, the user can choose different reverb and equalization presets to optimize the performance of classical or rock music.

6 is a block diagram of the audio amplifier 90 which depends on the audio system configuration within the audio system 32 or within the audio amplifier enclosure 70 is included. The audio amplifier 90 receives audio signals from the guitar 20 through the audio cable 26 , The audio amplifier 90 carries a gain and other signal processing functions such. B. equalization, filtering, sound effects and user-defined modules on the audio signal to adjust the power level and otherwise improve the signal characteristics for the listening experience.

To accommodate the signal processing requirements according to the dynamic content of the audio source, the audio amplifier uses 90 a feature of dynamic adaptive intelligence that Includes frequency domain analysis and time domain analysis of the audio signal on a frame by frame basis, and automatically and adaptively controls the operation of the signal processing functions and settings within the audio amplifier for optimal sound reproduction. Each frame contains a predetermined number of samples of the audio signal, e.g. B. 32-1024 samples per frame. Each incoming frame of the audio signal is detected on a frame-by-frame basis and analyzed to determine its time domain and frequency domain content and characteristics. The incoming frames of the audio signal are compared to a database of fixed or learned note signatures to determine a best match or closest correlation of the incoming frame with the database of note signatures. The note signatures from the database contain control parameters to the signal processing components of the audio amplifier 90 to configure. The most suitable note signature controls the audio amplifier 90 in real-time to continuously and automatically adjust the signal processing functions for optimal sound reproduction. For example, based on the note signature, the gain of the audio signal for that particular frame of the audio signal may be automatically increased or decreased. Presets and sound effects can be automatically turned on or removed for the note being played. The next frame in the sequence may be associated with the same note that matches the same note signature in the database, or the next frame in the sequence may be associated with a different note that matches another corresponding note signature in the database. Each frame of the audio signal is detected and compared with a note signature, which in turn controls the operation of the signal processing function within the audio amplifier 90 for optimal sound reproduction controls. The signal processing function of the audio amplifier 90 is set according to the most appropriate note signature corresponding to each individual incoming frame of the audio signal to improve its reproduction.

The feature of adaptive intelligence of the audio amplifier 90 can learn attributes of each note of the audio signal and make adjustments based on user feedback. For example, if the user desires more or less gain or equalization or the introduction of a particular sound effect for a given note, then the audio amplifier incorporates these user preferences into the control parameters of the signal processing function to achieve optimal sound reproduction. The database of note signatures with correlated control parameters performs real-time adjustments to the signal processing function. The user can define audio modules, effects and settings in the database of the audio amplifier 90 to get integrated. With adaptive intelligence, the audio amplifier can 90 Detect sound modules and settings and automatically apply to the audio signal based on the existing note signature. The audio amplifier 90 can interpolate between similar matching note signatures as needed to select the best choice for the current signal processing function.

With continued with 6 has the audio amplifier 90 a signal processing path for the audio signal with a pre-filter block 92 , a pre-effect block 94 a block 96 for non-linear effects, user-defined modules 98 , a after effect block 100 , a postfilter block 102 and a power amplification block 104 on. The pre-filter block 92 and the postfilter block 102 see different filter functions such. B. low pass filtering and bandpass filtering of the audio signal before. The prefiltering and postfiltering may include tone equalization functions over various frequency ranges to enhance or attenuate the levels of specific frequencies without affecting adjacent frequencies, such as, for example: B. Bass frequency adjustment and Höhenfrequenzeinstellung. Tone equalization may, for example, use stepwise equalization to amplify or attenuate all frequencies above or below a target or fundamental frequency, bell equalization to amplify or attenuate a narrow range of frequencies around a target or fundamental frequency, graphic equalization or parameter equalization. The pre-effect block 94 and the after effect block 100 introduce sound effects into the audio signal, such as For example, reverb, delays, multiple overlays, wah, auto volume, phase shifter, humming suppressor, squelch, vibrato, pitch shift, tremolo, and dynamic compression. The block 96 for nonlinear effects, non-linear effects introduce into the audio signal, such as: M modeling, distortion, clipping, blur, and modulation. The block 98 for user defined modules allows the user to define custom signal processing functions, such as: B. adding accompaniment instruments, vocals, and synthesizer options. The power amplifier block 104 provides power amplification or attenuation of the audio signal. The audio signal after signal processing becomes the speakers in the audio system 32 or to the speakers 82 in the case 72 directed.

The pre-filter block 92 , the pre-effect block 94 , the block 96 for non-linear effects, the user-defined modules 98 , the after effect block 100 , the post filter block 102 and the power amplification block 104 within the audio amplifier 90 are with the front panel 30 in 4 or the front panel 78 in 5 selectable and controllable. By turning the buttons 76 at the front panel 78 or under Using the LCD 60 , the function mode keys 62 , the selection buttons 64 and the adjustment knob or dial 66 of the programmable control panel 54 the user can control the operation of the signal processing functions within the audio amplifier 90 direct control.

The audio signal can come from a variety of audio sources, such. B. musical instruments or singing. The instrument may be an electric guitar, a bass guitar, a violin, a horn, a brass instrument, drums, a wind instrument, a piano, an electric keyboard, a drum kit, or other instruments capable of producing electrical signals that Represent sound content. The audio signal may come from an audio microphone operated by a man or woman with vocal ranges, including soprano, mezzo soprano, alto, tenor, baritone and bass. In the present discussion, the instrument is a guitar 20 , especially an electric bass guitar. When the strings 24 the bass guitar 20 With the finger of the musician or a guitar pick, the string starts a strong vibration or vibration coming from the pickup 22 is detected. The string vibration attenuates over time and returns to a steady state, assuming that the string is not re-excited before the vibration ceases. The initial stimulation of the strings 24 is known as the deployment phase. The deployment phase is followed by a hold phase during which the string vibration remains relatively strong. A decay phase follows the hold phase as the string vibration dampens, and finally a release phase as the string returns to a steady state. The pickup 22 converts string vibrations during the insertion phase, the holding phase, the decay phase and the release phase into an electrical signal, ie the analog audio signal, with an initial and then falling amplitude with a fundamental frequency and harmonics of the fundamental frequency. 7a - 7b represent amplitude responses of the audio signal in the time domain according to the use phase and holding phase and in dependence on the figure of the decay phase and release phase of the strings 24 in different game modes. In 7b The next deployment phase will begin before the completion of the previous cooldown or even the start of the release phase.

The artist can use a variety of playing styles when playing the bass guitar 20 plays. For example, the artist can arrange his hand near the neck pickup or bridge pickup and the strings 24 with finger plucking, which is known as "fingering style", for modern pop, rhythm and blues and avant-garde styles. The artist can do the strings 24 Beat your fingers or palm with what is known as "stroke style" for modern jazz, funk, rhythm and blues, and rock styles. The artist can do the strings 24 stimulate with the thumb, which is known as "thumb style", for Motown Rhythm and Blues. The artist can do the strings 24 with two hands, each hand picking off notes known as the "tap style" for avant-garde and modern jazz styles. In other game styles it is known that artists fingering accessories such. B. use a plectrum or a rod. In any case, the strings vibrate 24 with a specific amplitude and frequency and generate a unique audio signal according to the string vibration phases, such. In 7a and 7b shown.

6 further provides the dynamic adaptive intelligence control of the audio amplifier 90 A main purpose of the feature of adaptive intelligence of the audio amplifier 90 consists of detecting and isolating the frequency domain characteristics and time domain characteristics of the audio signal on a frame by frame basis and using this information to control the operation of the amplifier's signal processing function. The audio signal from the audio cable 26 becomes the frequency domain and time domain analysis block 110 directed. The output of the block 110 becomes the note signature block 112 passed and the output of the block 112 becomes the block 114 directed for adaptive intelligence control. The functions of the blocks 110 . 112 and 114 are discussed in turn.

8th represents another detail of the frequency domain and time domain analysis block 110 including the audio sample block 116 , the frequency domain analysis block 120 and the time domain analysis block 122 , The analog audio signal is sent to the audio sampling block 116 to hand over. The audio sampling block 116 samples the analog audio signal, eg. 32 to 1024 samples per second, using an analog-to-digital (A / D) converter. The sampled audio signal 118 is divided into a series of temporally progressive frames (frame 1 to frame n) each containing a predetermined number of samples of the audio signal. 9a shows the frame 1, the 64 samples of the audio signal 118 in the time sequence, frame 2 contains the next 64 samples of the audio signal 118 in the time sequence, the frame 3 containing the next 64 samples of the audio signal 118 in the time sequence, and so on up to the frame n, of the 64 samples of the audio signal 118 in the time sequence contains. 9b shows overlapping windows 119 of frames 1-n used in the time domain frequency domain conversion as in 15 described. The frames 1-n of the sampled audio signal 118 become the frequency domain analysis block 120 and time domain analysis block 122 directed.

10 represents another detail of the time domain analysis block 122 with the energy level insulation block 124 representing the energy level of each frame of the sampled audio signal 118 isolated in several frequency bands. In 11 processes the energy level isolation block 124 every frame of the sampled audio signal 118 in the time sequence through the filter frequency band 130 - 130c to separate and isolate specific frequencies of the audio signal. The filter frequency bands 130a - 130c can isolate specific frequency bands in the audio range of 100-10000 Hz. In one embodiment, the filter frequency band is 130a a bandpass filter having a passband centered at 100 Hz, the filter frequency band 130b is a bandpass filter with a passband centered at 500 Hz and the filter frequency band 130c is a bandpass filter with a passband centered at 1000 Hz. The output of the filter frequency band 130a contains the energy level of the sampled audio signal 118 which is centered at 100 Hz. The output of the filter frequency band 130b contains the energy level of the sampled audio signal 118 which is centered at 500 Hz. The output of the filter frequency band 130c contains the energy level of the sampled audio signal 118 which is centered at 1000 Hz. The output of other filter frequency bands each contains the energy level of the sampled audio signal 118 for a specific band. A top detector 132a monitors and stores peak energy levels of the sampled audio signal 118 that are centered at 100 Hz. A top detector 132b monitors and stores peak energy levels of the sampled audio signal 118 that are centered at 500 Hz. A top detector 132c monitors and stores peak energy levels of the sampled audio signal 118 that are centered at 1000 Hz. A smoothing filter 134a removes interfering components and otherwise stabilizes the peak energy levels of the sampled audio signal 118 that are centered at 100 Hz. A smoothing filter 134b removes interfering components and otherwise stabilizes the peak energy levels of the sampled audio signal 118 that are centered at 500 Hz. A smoothing filter 134c Removes interfering components of the peak power levels and otherwise stabilizes the sampled audio signal 118 that are centered at 1000 Hz. The output of the smoothing filters 134a - 134c is the energy level function E (m, n) for each frequency band 1-m in each frame n of the sampled audio signal 118 ,

The time domain analysis block 122 from 8th also includes a distress detector block 125 , as in 10 shown. The block 125 detects the beginning of each note and divides the sampled audio signal into discrete segments, each segment beginning with the beginning of the note, comprising a plurality of frames of the sampled audio signal and terminating with the beginning of the next note. In the present embodiment, each discrete segment of the sampled audio signal corresponds to a single note of music. The distress detector block 125 arranges the deployment phase of the strings 24 as the beginning of a note too. That is, the deployment phase of the vibrating string 24 on the guitar 20 coincides with the detection of a specific note. For other instruments, the emergency detection is associated with another physical act by the artist, e.g. Pressing the key of a piano or an electric keyboard, exciting the string of a harp, exhaling air into a horn while pressing one or more keys on the horn, or hitting the surface of a drum with a drumstick. In any case, the distress detector block monitors 125 the dynamic time domain content of the sampled audio signal 118 and identifies the beginning of a note.

12 shows another detail of the distress detector block 125 with the deployment detector 136 , Once the energy level function E (m, n) for each frequency band 1-m of the sampled audio signal 118 is determined, the energy levels 1-m of a frame n-1 in the block 138 of the deployment detector 136 saved; as in 13 shown. The energy levels of frequency bands 1-m for the next frame n of the sampled audio signal 118 as through the filter frequency bands 130a - 130c , Top detectors 132a - 132c and smoothing filters 134a - 134c certainly, be in the block 140 of the deployment detector 136 saved. The difference block 142 determines a difference between the energy levels of respective bands of the current frame n and the previous frame n-1. The energy level of the frequency band 1 for the frame n-1 is subtracted, for example, from the energy level of the frequency band 1 for the frame n. The energy level of the frequency band 2 for the frame n-1 is subtracted from the energy level of the frequency band 2 for the frame n. The energy level of the frequency band m for the frame n-1 is subtracted from the energy level of the frequency band m for the frame n. The difference of the energy levels for each frequency band 1-m of the frame n and frame n-1 will be in the summer 144 summed.

The summer 144 It accumulates the difference of the energy levels E (m, n) of each frequency band 1-m of the frame n and the frame n-1. The beginning of a note occurs when the total amount of differences in energy levels E (m, n) over the entire monitored frequency bands 1-m for the sampled audio signal 118 exceeds a predetermined threshold. A comparator 146 compares the output of the summer 144 with a threshold 148 , If the output of the summer 144 is greater than the threshold 148 , then the accumulation of differences exceeds the energy level E (m, n) over the entire frequency spectrum for the sampled audio signal 118 the threshold 148 and the beginning of a Note is detected in the current frame n. If the output of the summer 144 is less than the threshold 148 , then no beginning of a note is detected.

At the conclusion of each frame has the deployment detector 136 identifies whether the current frame contains the beginning of a note or whether the current frame does not contain a beginning of a note. On the basis of the summation of differences of energy levels E (m, n) of the sampled audio signal 118 across the whole spectrum of frequency bands 1-m, which exceeds the threshold 148 exceeds, the deployment detector 136 for example, the frame 1 of 9a as having identified the beginning of a note while frame 2 and frame 3 of 9a have no beginning of a grade. 7a represents the beginning of a note at the point 150 in frame 1 (based on the energy levels E (m, n) of the sampled audio signal within the frequency bands 1-m) and no beginning of a note in frame 2 or frame 3. 7a indicates another beginning detection of a note at the point 152 on. 7b shows start detections of a note at the points 154 . 156 and 158 ,

14 represents a further embodiment of the deployment detector 136 as the energy levels E (m, n) with the summer 160 summing up directly. The summer 160 It accumulates the energy levels E (m, n) of the frame n in each frequency band 1-m for the sampled audio signal 118 , The beginning of a note occurs when the total amount of energy levels E (m, n) over the entire monitored frequency bands 1-m for the sampled audio signal 118 exceeds a predetermined threshold. A comparator 162 compares the output of the summer 160 with a threshold 164 , If the output of the summer 160 is greater than the threshold 164 , then the accumulation exceeds the energy level E (m, n) over the entire frequency spectrum for the sampled audio signal 118 the threshold 164 and the beginning of a note is detected in the current frame n. If the output of the summer 160 is less than the threshold 164 , then no beginning of a note is detected.

At the conclusion of each frame has the deployment detector 136 identifies whether the current frame contains the beginning of a note or whether the current frame does not contain a beginning of a note. On the basis of the summation of energy levels E (m, n) of the sampled audio signal 118 within the frequency bands 1-m, which is the threshold 164 exceeds, the deployment detector 136 for example, the frame 1 of 9a as having identified the beginning of a note while frame 2 and frame 3 of 9a have no beginning of a grade.

Equation (1) provides another illustration of the beginning detection of a note. g (m, n) = max (0, [E (m, n) / E (m, n-1)] - 1) (1) where: g (m, n) is a maximum function of energy levels above n frames of
m is frequency bands,
E (m, n) is the energy level of the frame n of the frequency band m
E (m, n-1) is the energy level of the frame n-1 of the frequency band m.

The function g (m, n) has a value for each frequency band 1-m and each frame 1-n. When the ratio of E (m, n) / E (m, n-1), i. H. the energy level of the band m in the frame n to the energy level of the band m in the frame n-1 is less than one, then [E (m, n) / E (m, n-1)] -1 is negative. The energy level of band m in frame n is not greater than the energy level of band m in frame n-1. The function g (m, n) is zero, indicating no initiation of the insert phase and therefore no detection of the beginning of a note. When the ratio of E (m, n) / E (m, n-1), i. H. the energy level of the band m in the frame n to the energy level of the band m in the frame n-1 is greater than one (for example, a value of two), then [E (m, n) / E (m, n-1)] - 1 positive, d. H. a value of one. The energy level of band m in frame n is greater than the energy level of band m in frame n-1. The function g (m, n) is the positive value of [E (m, n) / E (m, n-1)] - 1, which indicates the initiation of the insertion phase and a possible detection of the beginning of a note.

If you are too 12 returns, the deployment detector directs 136 the beginning detection of a note to the silence gate 166 , to the repeat gate 168 and to the noise gate 170 , Not every beginning detection of a note is true. The silence gate 166 monitors the energy levels E (m, n) of the sampled audio signal 118 after the beginning detection of a note. When the energy levels E (m, n) of the sampled audio signal 118 are low after the start detection of a note due to silence, e.g. For example, -45 dB, then the energy levels E (m, n) of the sampled audio signal 118 that have triggered the beginning of a note, considered as phony and retired. For example, the artist may mistakenly choose one or more of the strings 24 have touched without deliberately playing a note or a chord. The energy level E (m, n) of the sampled audio signal 118 that may have resulted from the accidental contact may have been sufficient to detect the beginning of a note, however, since playing does not continue, ie the energy levels E (m, n) of the sampled audio signal 118 after the beginning detection of a note indicates silence, the start detection is retired.

The repeat gate 168 monitors the number of start detections that occur within a time period. If multiple starts of a note within a repeat detection time period, e.g. B. 50 milliseconds (ms), are detected, then only the first start detection is recorded. That is, any subsequent start of a note detected after the first start detection within the repeat detection period is discarded.

The noise gate 170 monitors the energy levels E (m, n) of the sampled audio signal 118 around the beginning detection of a note. When the energy levels E (m, n) of the sampled audio signal 118 to the beginning detection of a note are generally in the quiet area, z. For example, if the energy levels E (m, n) are -90 dB, then the onset detection is considered questionable and rejected as unreliable.

The time domain analysis block 122 from 8th also includes a note tip insert block 172 , as in 10 shown. The block 172 uses the energy function E (m, n) to calculate the time from the beginning detection of a note to the peak energy level of the note during the use phase or holding phase of the string vibration before the energy level drops across all frequency bands 1-m, ie a summation of frequency bands 1-m to determine. The beginning detection of a note is by the mission detector 136 certainly. The peak energy level is the maximum value of the energy function E (m, n) during the insertion phase or holding phase of the string vibration before the energy level drops across all frequency bands 1-m. The peak energy levels are framed in the peak detectors 132a - 132c supervised. The peak energy level may occur in the same frame as the start detection or in a subsequent frame. The headset is a time domain parameter or time domain characteristic of each frame n for all frequency bands 1-m and is considered to be the value in the runtime matrix 174 stored on a frame by frame basis.

The music head release block 176 uses the energy function E (m, n) to determine the time from the beginning detection of a note to a lower energy level during the decay phase or release phase of the note over all frequency bands 1-m, ie a summation of frequency bands 1-m. The beginning detection of a note is by the mission detector 136 certainly. The lower energy levels are frame by frame in the peak detectors 132a - 132c supervised. In one embodiment, the lower energy level is -3 dB from the peak energy level over all frequency bands 1-m. The note spike release is a time domain parameter or time domain characteristic of each frame n for all frequency bands 1-m and is used as the value in the phone time matrix 174 stored on a frame by frame basis.

A multi-band tip insert block 178 uses the energy function E (m, n) to determine the time from the beginning detection of a note to the peak energy level of the note during the use phase or holding phase of the string vibration before the energy level drops for each specific frequency band 1-m. The beginning detection of a note is by the mission detector 136 certainly. The peak power level is the maximum value during the use phase or holding phase of the string vibration before the energy level drops in each specific frequency band 1-m. The peak energy level is frame-by-frame in the peak detectors 132a - 132c supervised. The peak energy level may occur in the same frame as the start detection or in a subsequent frame. The multi-band peak use is a time domain parameter or time domain characteristic of each frame n for each frequency band 1-m and is taken as the value in the runtime matrix 174 stored on a frame by frame basis.

A multi-band tip release block 180 uses the energy function E (m, n) to determine the time from the beginning detection of a note to a lower energy level during the decay phase or release phase of the note in each specific frequency band 1-m. The beginning detection of a note is by the mission detector 136 certainly. The lower energy level is frame-by-frame in the peak detectors 132a - 132c supervised. In one embodiment, the lower energy level is -3 dB from the peak energy level in each frequency band 1-m. The multi-band peak enable is a time domain parameter or time domain characteristic of each frame n for each frequency band 1-m and is taken as the value in the runtime matrix 174 stored on a frame by frame basis.

The shock detector 182 monitors the energy function E (m, n) in each frame 1-n over frequency bands 1-m to determine the occurrence of a beat style event, ie the artist has the strings 24 beaten with his fingers or his palm. A hit event is by a sharp spike in the Energy level during a frame marked in the use phase of the note. For example, an impact event causes a peak of 6 dB in the energy level above and above the energy level in the next frame in the deployment phase. The peak of 6 dB in the energy level is interpreted as a beating event. The beat detector is a time domain parameter or time domain characteristic of each frame n for all frequency bands 1-m and is considered to be the value in the runtime matrix 174 stored on a frame by frame basis.

The tempo detector 184 monitors the energy function E (m, n) in each frame 1-n over frequency bands 1-m to determine the time interval between the beginning detection of adjacent notes, ie the duration of each note. The tempo detector is a time domain parameter or time domain characteristic of each frame n for all frequency bands 1-m and is considered to be the value in the runtime matrix 174 stored on a frame by frame basis.

wobei: X_m, das Audiosignal im Frequenzbereich ist
x(n) das Audioeingangssignal des m-ten Rahmens ist
m die aktuelle Nummer des Rahmens ist
k der Frequenzintervallbereich ist
N die STFT-Größe ist.The frequency domain analysis block 120 in 8th includes an STFT block 185 , as in 15 shown. The block 185 performs a time domain frequency domain conversion on a frame by frame basis of the sampled audio signal 118 using a short-time Fourier transform (STFT) with constant overlap addition (COLA) or another fast Fourier transform (FFT). The COLA-STFT 185 performs a time domain frequency domain conversion using overlap analysis windows 119 through, as in 9b shown. The sampling windows 119 overlap by a predetermined number of samples of the audio signal, known as a hop size, for additional sample points in the COLA-STFT analysis to ensure that the data is weighted equally in successive frames. Equation (2) provides a general format of the time domain frequency domain conversion on the sampled audio signal 118 ,

where: X _{m is} the audio signal in the frequency domain
x (n) is the audio input signal of the mth frame
m is the current number of the frame
k is the frequency interval range
N is the STFT size.

The frequency domain analysis block 120 from 8th also includes a distress detector block 186 , as in 15 shown. Once the sampled audio signal 118 is in the frequency range, the block detects 186 the beginning of each note, and arranging the sampled audio signal into discrete segments, each segment beginning with the beginning of the note, comprising a plurality of frames of the sampled audio signal and terminating with the beginning of the next note. In the present embodiment, each discrete segment corresponds to the sampled audio signal 118 a single note of music. The distress detector block 186 arranges the deployment phase of the string 24 as the beginning of a note too. That is, the deployment phase of the vibrating string 24 on the guitar 20 coincides with the detection of a specific note. For other instruments, the emergency detection is associated with another physical act by the artist, e.g. Pressing the key of a piano or an electronic keyboard, exciting the string of a harp, exhaling air into a horn while pressing one or more keys on the horn, or hitting the surface of a drum with a drumstick. In any case, the distress detector block monitors 186 the dynamic frequency domain content of the sampled audio signal 118 and identifies the beginning of a note.

16 shows another detail of the frequency domain distress detector block 186 with the energy level insulation block 187 representing the energy level of each frame of the sampled audio signal 118 isolated in several frequency interval ranges.

In 17 processes the energy level isolation block 187 every frame of the sampled audio signal 118 in terms of the frequency range through filter frequency interval ranges 188a - 188c to separate and isolate specific frequencies of the audio signal. The filter frequency interval ranges 188a - 188c can isolate specific frequency bands in the audio range of 100-10000 Hz. In one embodiment, the filter frequency interval range is 188a centered at 100 Hz, the filter frequency interval range 188b is centered at 500 Hz and the filter frequency interval range 188c is centered at 1000 Hz. The edition of the Filter frequency interval range 188a contains the energy level of the sampled audio signal 118 which is centered at 100 Hz. The output of the filter frequency interval range 188b contains the energy level of the sampled audio signal 118 which is centered at 500 Hz. The output of the filter frequency interval range 188c contains the energy level of the sampled audio signal 118 which is centered at 1000 Hz. The output of other filter frequency interval ranges each contains the energy level of the sampled audio signal 118 for a given specific band. The top detector 189a monitors and stores the peak energy levels of the sampled audio signal 118 that are centered at 100 Hz. The top detector 189b monitors and stores the peak energy levels of the sampled audio signal 118 that are centered at 500 Hz. The top detector 189c monitors and stores the peak energy levels of the sampled audio signal 118 that are centered at 1000 Hz. The smoothing filter 190a removes interfering components and otherwise stabilizes the peak energy levels of the sampled audio signal 118 that are centered at 100 Hz. The smoothing filter 190b removes interfering components and otherwise stabilizes the peak energy levels of the sampled audio signal 118 that are centered at 500 Hz. The smoothing filter 190c Removes interfering components of the peak power levels and otherwise stabilizes the sampled audio signal 118 that are centered at 1000 Hz. The output of the smoothing filters 190a - 190c is the energy level function E (m, n) for each frame n in each frequency interval range 1-m of the sampled audio signal 118 ,

The energy levels E (m, n) of a frame n-1 are in the block 191 of the deployment detector 192 stored as in 18 shown. The energy levels of each frequency interval range 1-m for the next frame n of the sampled audio signal 118 as through the filter frequency interval ranges 188a - 188c , the top detectors 189a - 189c and the smoothing filters 190a - 190c certainly, be in the block 193 of the deployment detector 192 saved. The difference block 194 determines a difference between the energy levels of respective interval areas of the present frame n and the previous frame n-1. For example, the energy level of the frequency interval range 1 for the frame n-1 is subtracted from the energy level of the frequency interval range 1 for the frame n. The energy level of the frequency interval range 2 for the frame n-1 is subtracted from the energy level of the frequency interval range 2 for the frame n. The energy level of the frequency interval range m for the frame n-1 is subtracted from the energy level of the frequency interval range m for the frame n. The difference of the energy levels for each frequency interval range 1-m of frame n and frame n-1 will be in the summer 195 summed.

The summer 195 It accumulates the difference of the energy levels E (m, n) of each frequency interval range 1-m of the frame n and the frame n-1. The beginning of a note occurs when the total amount of differences in energy levels E (m, n) over the entire monitored frequency interval ranges 1-m for the sampled audio signal 118 exceeds a predetermined threshold. A comparator 196 compares the output of the summer 195 with a threshold 197 , If the output of the summer 195 is greater than the threshold 197 , then the accumulation of differences exceeds the energy level E (m, n) over the entire frequency spectrum for the sampled audio signal 118 the threshold 197 and the beginning of a note is detected in the current frame n. If the output of the summer 195 is less than the threshold 197 , then no beginning of a note is detected.

At the conclusion of each frame has the deployment detector 192 identifies whether the current frame contains the beginning of a note or whether the current frame does not contain a beginning of a note. On the basis of the summation of differences of the energy levels E (m, n) of the sampled audio signal 118 across the whole spectrum of frequency interval ranges 1-m, which is the threshold 197 can exceed the deployment detector 192 for example, the frame 1 of 9a as having identified the beginning of a note while frame 2 and frame 3 of 9a have no beginning of a grade. 7a represents the beginning of a note at the point 150 in frame 1 (based on the energy levels E (m, n) of the sampled audio signal within the frequency interval ranges 1-m) and no beginning of a note in frame 2 or frame 3. 7a indicates another beginning detection of a note at the point 152 on. 7b shows start detections of a note at the points 154 . 156 and 158 ,

19 represents a further embodiment of the deployment detector 192 as the energy levels E (m, n) with the summer 198 summing up directly. The summer 198 It accumulates the energy levels E (m, n) of each frame 1-n and each frequency interval range 1-m for the sampled audio signal 118 , The beginning of a note occurs when the total amount of energy levels E (m, n) over the entire monitored frequency interval ranges 1-m for the sampled audio signal 118 exceeds a predetermined threshold. The comparator 199 compares the output of the summer 198 with a threshold 200 , If the output of the summer 198 is greater than the threshold 200 , then the accumulation of energy levels E (m, n) over the entire frequency spectrum for the sampled audio signal exceeds 118 the threshold 200 and the beginning of a note is detected in the current frame n. If the output of the summer 198 is less than the threshold 200 , then no beginning of a note is detected.

At the conclusion of the frame has the deployment detector 192 identifies whether the current frame contains the beginning of a note or whether the current frame does not contain a beginning of a note. On the basis of the summation of energy levels E (m, n) of the sampled audio signal 118 within the frequency interval ranges 1-m, which is the threshold 200 exceeds, the deployment detector 192 for example, the frame 1 of 9a as having identified the beginning of a note while frame 2 and frame 3 of 9a have no beginning of a grade.

If you are too 16 returns, the deployment detector directs 192 the beginning detection of a note to the silence gate 201 , to the repeat gate 202 and to the noise gate 203 , Not every beginning detection of a note is true. The silence gate 201 monitors the energy levels E (m, n) of the sampled audio signal 118 after the beginning detection of a note. When the energy levels E (m, n) of the sampled audio signal 118 are low after the start detection of a note due to silence, e.g. For example, -45 dB, then the energy levels E (m, n) of the sampled audio signal 118 that have triggered the beginning of a note, considered as phony and retired. For example, the artist may mistakenly choose one or more of the strings 24 have touched without deliberately playing a note or a chord. The energy levels E (m, n) of the sampled audio signal 118 that may have resulted from the accidental contact may have been sufficient to detect the beginning of a note, however, since playing does not continue, ie the energy levels E (m, n) of the sampled audio signal 118 after the beginning detection of a note indicates silence, the start detection is retired.

Equation (1) provides another illustration of the beginning detection of a note. The function g (m, n) has a value for each frequency interval range 1-m and every frame 1-n. When the ratio of E (m, n) / E (m, n-1), i. H. the energy level of the interval range m in the frame n to the energy level of the interval range m in the frame n-1 is less than one, then [E (m, n) / E (m, n-1)] -1 is negative. The energy level of the interval range m in frame n is not greater than the energy level of the interval range m in frame n-1. The function g (m, n) is zero, indicating no initiation of the insert phase and therefore no detection of the beginning of a note. When the ratio of E (m, n) / E (m, n-1), i. H. the energy level of the interval range m in the frame n to the energy level of the interval range m in the frame n-1 is greater than one (for example, a value of two), then [E (m, n) / E (m, n-1)] - 1 positive, d. H. a value of one. The energy level of the interval range m in frame n is greater than the energy level of the interval range m in frame n-1. The function g (m, n) is the positive value of [E (m, n) / E (m, n-1)] - 1, which indicates the initiation of the insertion phase and a possible detection of the beginning of a note.

The repeat gate 202 monitors the number of start detections that occur within a time period. If multiple starts of a note within the repeat detection time period, e.g. B. 50 ms, are detected, then only the first start detection is recorded. That is, any subsequent start of a note detected after the first start detection within the repeat detection period is discarded.

The noise gate 203 monitors the energy levels E (m, n) of the sampled audio signal 118 around the beginning detection of a note. When the energy levels E (m, n) of the sampled audio signal 118 to the beginning detection of a note are generally in the quiet area, z. For example, if the energy levels E (m, n) are -90 dB, then the onset detection is considered questionable and rejected as unreliable.

If you are too 15 returns, determines the harmonic ratio block 204 a ratio of the energy levels of different frequency harmonics in the sampled frequency domain audio signal 118 during the mission phase or holding phase of the note on a frame by frame basis. Alternatively, the harmonic ratio monitors a fundamental frequency and harmonic of the fundamental frequency. In one embodiment, to monitor a beat style, the frequency domain energy level of the sampled audio signal 118 at the fundamental frequency of 200 Hz of the beat and the harmonic of 4000 Hz of the fundamental frequency measured during the use phase of the note. The ratio of the frequency domain power levels 4000/200 Hz during the use phase of the note for each frame 1-n is the harmonic ratio. Other frequency harmonics ratios in the mission phase of the note may be monitored on a frame by frame basis. The block 204 determines the rate of change of energy levels in harmonic ratio, ie, how fast the energy levels increase or decrease relative to each frame during the use phase of the note. The harmonic ratio is a frequency domain parameter or a Frequency domain characteristic of each frame n and is used as a value in the runtime matrix 174 stored on a frame by frame basis.

The harmonic release ratio block 205 determines a ratio of the energy levels of different frequency harmonics of the sampled frequency domain audio signal 118 during the decay phase or release phase of the note on a frame by frame basis. Alternatively, the harmonic release ratio monitors a fundamental frequency and harmonic of the fundamental frequency. In one embodiment, to monitor a beat style, the frequency domain energy level of the sampled audio signal 118 at the fundamental frequency of 200 Hz of the beat and the harmonic of 4000 Hz of the fundamental frequency measured during the release phase of the note. The ratio of the frequency domain energy levels 4000/200 Hz during the release phase of the note for each frame 1-n is the harmonic release ratio. Other frequency harmonic ratios in the release phase of the note may be monitored on a frame by frame basis. The block 205 determines the rate of change of energy levels in harmonic ratio, ie how fast the energy levels increase or decrease, relative to each frame during the release phase of the note. The harmonic release ratio is a frequency domain parameter or frequency domain characteristic of each frame n and is considered to be the value in the runtime matrix 174 stored on a frame by frame basis.

An empty and damping factor block 206 monitors the energy levels of the sampled frequency domain audio signal for the occurrence of an empty or attenuated condition of the strings 24 , A damping state of the strings 24 occurs when the artist continually presses his fingers against the strings, usually near the bridge of the guitar 20 , The finger pressure on the strings 24 dampens or weakens the string vibration quickly. An empty state is the absence of a damping state, ie no finger pressure or other artificial damping of strings 24 so that the string vibration naturally fades away.

In the attenuation state, the sustain phase and decay phase of the note is significantly shorter due to the induced attenuation than natural decay in the empty state. A lack of high frequency content and a rapid decrease in the frequency domain energy levels of the sampled audio signal 118 indicates the damping condition. A high frequency value and a natural decay of the frequency domain energy levels of the sampled audio signal 118 indicates the empty state. The blanking and attenuation factor is a frequency domain parameter or frequency domain characteristic of each frame n and is taken as the value in the runtime matrix 174 stored on a frame by frame basis.

A neck and bridge factor block 207 monitors the energy levels of the sampled frequency domain audio signal 118 on the appearance of Halsspiel or bridge play by the artist. Half play of strings 24 occurs when the artist pulls the strings near the neck of the guitar 20 stimulates. The bridge play of strings 24 occurs when the artist strings the strings near the bridge of the guitar 20 stimulates. When playing near the neck, a first frequency notch occurs around 100 Hz in the frequency domain response of the sampled audio signal 118 on. When playing near the land, a first frequency notch occurs around 500 Hz in the frequency domain response of the sampled audio signal 118 on. The appearance and location of a first notch in the frequency response indicates halftime or bridge play. The neck and land factor is a frequency domain parameter or frequency domain characteristic of each frame n and is considered to be the value in the runtime matrix 174 stored on a frame by frame basis.

A pitch detector block 208 monitors the energy levels of the sampled frequency domain audio signal 118 to determine the pitch of the note. The block 208 Records the fundamental frequency of the pitch. The pitch detector is a frequency domain parameter or frequency domain characteristic of each frame n and is considered to be a value in the runtime matrix 174 stored on a frame by frame basis.

The runtime matrix 174 contains the in the frequency domain analysis block 120 certain frequency domain parameters and those in the time domain analysis block 122 certain time domain parameters. Each time domain parameter and frequency domain parameter is a numerical parameter value PVn, j, which is in the runtime matrix 174 is stored on a frame by frame basis, where n is the frame and j is the parameter. For example, the headset parameter has the value PV1,1 in frame 1, the value PV2,1 in frame 2, and the value PVn, 1 in frame n; the note peak enable parameter has the value PV1,2 in frame 1, the value PV2,2 in frame 2 and the value PVn, 2 in frame n; the multi-band peak usage parameter has the value PV1,3 in frame 1, the value PV2,3 in frame 2, and the value PVn, 3 in frame n; and so on. Table 1 shows the runtime matrix 174 with the time domain and frequency domain parameter values PVn, j generated during runtime analysis. The time domain and frequency domain parameter values PVn, j are characteristic of specific notes and therefore useful in distinguishing between notes. parameter Frame 1 Frame 2 ... Frame n Note lace PV1,1 PV2,1 PVn, 1 Note tip release PV1,2 PV2,2 PV n, 2 Multiband lace PV1,3 PV2,3 PV n, 3 Multiband peak release PV1,4 PV2,4 PV n, 4 beat detector PV1,5 PV2,5 PV n, 5 speed detector PV1,6 PV2,6 PVn, 6 Harmonic use ratio PV1,7 PV2,7 PVn, 7 Harmonic release ratio PV1,8 PV2,8 PVn, 8 Empty and damping factor PV1,9 PV2,9 PV n, 9 Neck and bridge factor PV1,10 PV2,10 PV n, 10 pitch detector PV1,11 PV2,11 PV n, 11 Table 1 - Runtime matrix 174 with time domain parameters and frequency domain parameters from the runtime analysis

Table 2 shows a frame of the runtime matrix 174 wherein the time domain and frequency domain parameters provided by the frequency domain analysis block 120 and the time domain analysis block 122 numerical pattern values for an audio signal derived from a fingering style are assigned. The runtime matrix 174 contains time domain and frequency domain parameter values PVn, j for other frames of the audio signal that originate from fingering style, according to Table 1. parameter frame value Note lace 28 Note tip release 196 Multiband lace 31, 36, 33 Multiband peak release 193, 177, 122 beat detector 0 speed detector 42 Harmonic use ratio 0.26 Harmonic release ratio 0.85 Empty and damping factor 0.19 Neck and bridge factor 207 pitch detector 53 Table 2 - Time domain and frequency domain parameters from the runtime analysis of a fingering style frame

Table 3 shows a frame of the runtime matrix 174 wherein the time domain and frequency domain parameters provided by the frequency domain analysis block 120 and the time domain analysis block 122 generated numerical pattern values for an audio signal originating from a style of beat. The runtime matrix 174 contains time domain and frequency domain parameter values PVn, j for other frames of the audio signal derived from the beat style, according to Table 1. parameter frame value Note lace 6 Note tip release 33 Multiband lace 6, 4, 7 Multiband peak release 32, 29, 20 beat detector 1 speed detector 110 Harmonic use ratio 0.90 Harmonic release ratio 0.24 Empty and damping factor 0.76 Neck and bridge factor 881 pitch detector 479 Table 3 - Time domain parameters and frequency domain parameters from the runtime analysis of a beat style frame

With return to 6 becomes the database 112 in a memory component of the audio amplifier 90 and contains a plurality of note signature records 1, 2, 3, ... i, each note signature record having time domain parameters and frequency domain parameters corresponding to the runtime matrix 174 correspond. In addition, the note signature records 1-i include weighting factors 1, 2, 3, ... j for each time domain and frequency domain parameter and a plurality of control parameters 1, 2, 3, ... k.

20 shows the database 112 with time domain and frequency domain parameters 1-j for each note signature dataset 1-i, weighting factors 1-j for each note signature dataset 1-i and control parameters 1-k for each note signature dataset 1-i. Each note signature data set i is defined by the parameters 1-j and associated weights 1-j characteristic of the note associated with note signature i, and is used to form an incoming frame from the runtime matrix 174 to identify with the note signature i as best matching or closest correlated. Once the incoming frame from the runtime matrix 174 matched with a special note signature i, uses adaptive intelligence control 114 the control parameters 1-k of the appropriate note signature to the operating state of the signal processing blocks 92 - 104 of the audio amplifier 90 adjust. In a suitable note signature data set i, for example, the control parameter i, 1 sets the operating state of the pre-filter block 92 firmly; the control parameter i, 2 defines the operating state of the pre-effect block 94 firmly; the control parameter i, 3 sets the operating state of the block 96 for non-linear effects; the control parameter i, 4 defines the operating state of the modules defined by the user 98 firmly; the control parameter i, 5 defines the operating state of the after-effect block 100 firmly; the control parameter i, 6 sets the operating state of the postfilter block 102 firmly; and the control parameter i, 7 sets the operating state of the power amplification block 104 firmly.

The time domain parameters and frequency domain parameters 1-j in the note signature database 112 contain values specified by the manufacturer or entered by the user or learned over time by playing an instrument. The factory or the manufacturer of the audio amplifier 90 may initially set the values of the time domain and frequency domain parameters 1-j and weighting factors 1-j and control parameters 1-k. The user may set the time domain and frequency domain parameters 1-j, the weighting factors 1-j and the control parameters 1-k for each note signature 1-i in the database 112 directly using the computer 209 with the user interface screen or user interface display 210 , please refer 21 , to change. The values for the time domain and frequency domain parameters 1-j, de weighting factors 1-j and the control parameters 1-k are displayed on the interface screen 210 to allow the user to enter updated values.

In another embodiment, time domain and frequency domain parameters 1-j, weighting factors 1-j and control parameters 1-k may be used by the artist, the guitar 20 plays, be learned. The artist puts the audio amplifier 90 into a learning mode. The artist repeatedly plays the same note on the guitar 20 , For example, the artist takes a special note or beats a special note many times. The frequency domain analysis 120 and the time domain analysis 122 from 8th generate a runtime matrix 174 with associated frequency domain and time domain parameters 1-j each time the same note is played. A series of frequency domain and time domain parameters 1-j for the same note are accumulated and stored in the database 112 saved.

If the note is played repeatedly, the artist can manually adjust settings on the audio amplifier 90 via the front panel 78 carry out. The audio amplifier 90 learns the control parameters 1-k associated with the note through the settings of the signal processing blocks 92 - 104 as manually set by the artist. For example, the artist beats a note on the bass guitar 20 , Frequency domain parameters and time domain parameters for the beaten note are entered frame by frame in the database 112 saved. The artist puts the signal processing blocks 92 - 104 of the audio amplifier 90 by Front panel controls 78 manually, increases z. B. the gain of the audio signal in the gain block 104 or choose a sound effect in the pre-effect block 94 out. The settings of the signal processing blocks 92 - 104 , as manually set by the artist, are used as control parameters 1-k for the learned note signature in the database 112 saved. The artist hits the same note on the bass guitar 20 , Frequency domain parameters and time domain parameters for the same beat note will be in the database with the previous frequency domain and time domain parameters 1-j 112 collected. The artist puts the signal processing blocks 92 - 104 of the audio amplifier 90 through front panel controls 78 manually on, z. B. he provides the equalization of the audio signal in the pre-filter block 92 or select a sound effect in the block 96 for non-linear effects. The settings of the signal processing blocks 92 - 104 , as manually set by the artist, are used as control parameters 1-k for the learned note signature in the database 112 collected. The process travels to repeat-hit learning mode of the same note and manual adjustments to the signal processing blocks 92 - 104 of the audio amplifier 90 through the front panel controls 78 continued. When the learn mode is completed, the note signature record will be in the database 112 using the note signature parameters, which are an average of the frequency domain parameters and time domain parameters stored in the database 112 are collected, and an average of the control parameters 1-k, that of the manual settings of the signal processing blocks 92 - 104 of the audio amplifier 90 are taken and in the database 112 are collected, are defined. In one embodiment, the mean is a root mean square of the series of collected frequency domain and time domain parameters 1-j and the collected control parameter 1-k in the database 112 ,

Weighting factors 1-j may be learned by monitoring the learned time domain and frequency domain parameters 1-j and increasing or decreasing the weighting factors based on the proximity or statistical correlation of the comparison. If a particular parameter has a consistent statistical correlation, then the weighting factor for that parameter can be increased. If a particular parameter has a different statistical correlation, then the weighting factor for that parameter can be reduced.

Once the parameters 1-j, the weighting factors 1-j and the control parameters 1-k of the note signatures 1-i for the database 112 are fixed, the time domain and frequency domain parameters 1-j in the runtime matrix 174 on a frame by frame basis with each note signature 1-i to find a best match or closest correlation. In a normal game mode, the artist plays the guitar 20 to create a sequence of notes according to the melody being played. For each note, the runtime matrix becomes 174 on a frame by frame basis with time domain parameters and frequency domain parameters determined from a runtime analysis of the audio signal, as in 6 - 19 described, occupied.

The comparison between the runtime matrix 174 and the note signatures 1-i in the database 112 can be done in a variety of implementations. The time domain and frequency domain parameters 1-j in the runtime matrix 714 for example, in the time sequence with parameters 1-j for each note signature 1-i in the database individually 112 compared. The best match or closest correlation is for each frame of the runtime matrix 174 certainly. The block 114 for adaptive intelligence control uses the control parameters 1-k in the database 112 associated with the appropriate note signature to the operation of the signal processing blocks 92 - 104 of the audio amplifier 90 to control.

In another example, the time domain and frequency domain parameters 1-j in a predetermined number of frames of a note, less than all frames of a note, in the runtime matrix 174 with parameters 1-j for each note signature 1-i in the database 112 compared. In one embodiment, the time domain and frequency domain parameters 1-j in the first ten frames of each note in the runtime matrix 174 as determined by the beginning detection of the note, compared with parameters 1-j for each note signature 1-i. An average of the comparison between time domain and frequency domain parameters 1-j in each of the first ten frames of each note in the runtime matrix 174 and parameters 1-j for each note signature 1-i determines a best match or closest correlation to the frames in the runtime matrix 174 to identify as a special note associated with a note signature i. The block 114 for adaptive intelligence control uses the control parameters 1-k in the database 112 associated with the appropriate note signature to the operation of the signal processing blocks 92 - 104 of the audio amplifier 90 to control.

In an illustrative numerical example of the parameter comparison process, for a best match or closest correlation between the time domain and frequency domain parameters 1-j for each frame in the runtime matrix 174 and to determine parameters 1-j for each note signature 1-i, is shown in Table 4 Time domain and frequency domain parameters 1-j with sample parameter values for the note signature 1 (fingering style note) of the database 112 , Table 5 shows the time domain and frequency domain parameters 1-j with sample parameter values for the signature 2 (beat style note) of the database 112 , parameter value weighting Note lace 30 0.83 Note tip release 200 0.67 Multiband lace 30, 35, 33 0.72 Multiband peak release 200, 180, 120 0.45 beat detector 0 1.00 speed detector 50 0.38 Harmonic use ratio 0.25 0.88 Harmonic release ratio 0.80 0.61 Empty and damping factor 0.15 0.70 Neck and bridge factor 200 0.69 pitch detector 50 0.40 Table 4 - Time domain parameters and frequency domain parameters for note signature 1 (fingering style) parameter value weighting Note lace 5 0.80 Note tip release 40 0.71 Multiband lace 5, 4, 5 0.65 Multiband peak release 30, 25, 23 0.35 beat detector 1 1.00 speed detector 100 0.27 Harmonic use ratio 0.85 0.92 Harmonic release ratio 0.20 0.69 Empty and damping factor 0.65 0.74 Neck and bridge factor 1000 0.80 pitch detector 500 0.57 Table 5 - Time Range Parameters and Frequency Domain Parameters in the Note Signature 2 (Beat Style)

The time domain and frequency domain parameters 1-j for a frame in the runtime matrix 174 and the parameters 1-j in all the note signatures 1-i are successively compared on a basis and the differences are recorded. The headset usage parameter of frame 1 in the runtime matrix 174 for example, has a value of 28 (see Table 2), and the note-point usage parameter in note signature 1 has a value of 30 (see Table 4). 22 shows a recognition detector 211 with the comparison block 212 for determining the difference between the time domain and frequency domain parameters 1-j for a frame in the runtime matrix 174 and the parameters 1-j in the note signature i. The difference 30-28 between the frame 1 and the note signature 1 is in the recognition memory 213 saved. The note peak enable parameter of frame 1 in the runtime matrix 174 has a value of 196 (see Table 2) and the note-point enable parameter in note signature 1 has a value of 200 (see Table 4). The comparison block 212 determines the difference 200-196 and stores the difference in the recognition memory 213 , For each parameter of frame 1, the comparison block determines 212 the difference between the parameter value in the runtime matrix 174 and the parameter value in the note signature 1 and stores the difference in the recognition memory 213 , The differences between the parameters 1-j of the frame 1 and the parameters 1-j of the note signature 1 are summed to determine a total difference value between the parameters 1-j of the frame 1 and the parameters 1-j of the note signature 1.

Next, the note peak usage parameter of frame 1 in the runtime matrix 174 a value of 28 on (see Table 2) and the note-point use parameter in the note signature 2 has a value of 5 (see Table 5). The comparison block 212 determines the difference 5-28 and stores the difference between the frame 1 and the note signature 2 in the recognition memory 213 , The note peak enable parameter of frame 1 in the runtime matrix 174 has a value of 196 (see Table 2) and the note-point enable parameter in note signature 2 has a value of 40 (see Table 5). The comparison block 212 determines the difference 40-196 and stores the difference in the recognition memory 213 , For each parameter of frame 1, the comparison block determines 212 the difference between the parameter value in the runtime matrix 174 and the parameter value in the note signature 2 and stores the difference in the recognition memory 213 , The differences between the parameters 1-j in the runtime matrix 174 for the frame 1 and the parameters 1-j of the note signature 2 are summed to give an overall difference value between the parameters 1-j in the runtime matrix 174 for the frame 1 and the parameters 1-j of the note signature 2 to determine.

The time domain and frequency domain parameters 1-j in the runtime matrix 174 for frame 1, with the time domain and frequency domain parameters 1-in the remaining note signatures 3-i in the database 112 compared as described for the note signatures 1 and 2. The minimum total difference between the parameters 1-j in the runtime matrix 174 for the frame 1 and the parameters 1-j of the note signatures 1-i is the best match or closest correlation. In this case, the time domain and frequency domain parameters are 1-j in the runtime matrix 174 for the frame 1 is more closely aligned with the time domain and frequency domain parameters 1-j in the note signature 1. The frame 1 of the runtime matrix 174 is identified as the frame of a fingering style note.

With the time domain parameters and frequency domain parameters 1-j of frame 1 in the runtime matrix 174 that are generated by a played note that matches the note signature 1 uses the block 114 for adaptive intelligence control of 6 the control parameters 1-k in the database 112 associated with the appropriate note signature 1 for the operation of the signal processing blocks 92 - 104 of the audio amplifier 90 to control. The control parameter 1,1, the control parameter 1,2 to the control parameter 1, k under the note signature 1 each have a numerical value, z. Eg 1-10. The control parameter 1,1 has, for example, a value of 5 and sets the operating state of the pre-filter block 92 so strong that it has a 200 Hz low pass filter function; the control parameter 1,2 has a value of 7 and sets the operating state of the pre-effect block 94 fixed to turn on a reverberant sound effect; the control parameter 1,3 has a value of 9 and defines the operating state of the block 96 for non-linear effects, to introduce distortion; the control parameter 1.4 has a value of 1 and defines the operating state of the modules defined by the user 98 fixed to add a drum accompaniment; control parameter 1.5 has a value of 3 and defines the operating state of the after-effect block 100 to turn on a buzzer sound effect; the control parameter 1.6 has a value 4 and defines the operating state of the postfilter block 102 firm to allow a bell equalization; and the control parameter 1.7 has a value of 8 and sets the operating state of the power amplification block 104 fixed to increase the gain by 3 dB. The audio signal is passed through the pre-filter block 92 , the pre-effect block 94 , the block 96 for non-linear effects, the user-defined modules 98 , the after effect block 100 , the postfilter block 102 and the power amplification block 104 processed, each working as determined by the control parameter 1,1, the control parameter 1,2 to the control parameter 1, k of the note signature 1. The improved audio signal becomes the speaker in the case 24 or to the speaker 82 in the case 72 directed. The listener hears the reproduced audio signal in real time with characteristics determined by the dynamic content of the audio signal.

Next, the time domain and frequency domain parameters 1-j for the frame 2 in the runtime matrix 174 and the parameters 1-j in all the note signatures 1-i are successively compared on a basis and the differences are recorded. For each parameter 1-j of frame 2, the comparison block determines 212 the difference between the parameter value in the runtime matrix 174 and the parameter value in the note signature 1 and stores the difference in the recognition memory 213 , The differences between the parameters 1-j of the frame 2 and the parameters 1-j of the note signature i are summed to determine a total difference value between the parameters 1-j of the frame 2 and the parameters 1-j of the note signature i. The minimum total difference between the parameters 1-j of frame 2 of the runtime matrix 174 and the parameters 1-j of the note signatures 1-i is the best match or closest correlation. The frame 2 of the runtime matrix 174 is identified with the note signature with the minimum total difference between corresponding parameters. In this case, the time domain and frequency domain parameters 1-j of frame 2 are in the runtime matrix 174 more closely aligned to the time domain and frequency domain parameters 1-j in the note signature 1. The frame 2 of the runtime matrix 174 will serve as another framework for identified a fingering style note. The block 114 for adaptive intelligence control uses the control parameter 1-k in the database 112 that are associated with the appropriate note signature 1 to the operation of the. Signal processing blocks 92 - 104 of the audio amplifier 90 to control. The process continues for each frame n of the runtime matrix 174 continued.

In another numerical example, the note peak usage parameter of frame 1 in the runtime matrix 174 a value of 6 (see Table 3) and the note-point use parameter in note signature 1 has a value of 30 (see Table 4). The difference 30-6 between the frame 1 and the note signature 1 is in the recognition memory 213 saved. The note peak enable parameter of frame 1 in the runtime matrix 174 has a value of 33 (see Table 3) and the note-point enable parameter in note signature 1 has a value of 200 (see Table 4). The comparison block 212 determines the difference 200-33 and stores the difference in the recognition memory 213 , For each parameter of frame 1, the comparison block determines 212 the difference between the parameter value in the runtime matrix 174 and the parameter value in the note signature 1 and stores the difference in the recognition memory 213 , The difference between the parameters 1-j of frame 1 in the runtime matrix 174 and the parameters 1-j of the note signature 1 are summed to determine an overall difference value between the parameters 1-j of the frame 1 and the parameters 1-j of the note signature 1.

Next, the note peak usage parameter of frame 1 in the runtime matrix 174 a value of 6 (see Table 3) and the note-point usage parameter in note signature 2 has a value of 5 (see Table 5). The comparison block 212 determines the difference 5-6 and stores the difference in the recognition memory 213 , The note peak enable parameter of frame 1 in the runtime matrix 174 has a value of 33 (see Table 3) and the note-point enable parameter in note signature 2 has a value of 40 (see Table 5). The comparison block 212 determines the difference 40-33 and stores the difference in the recognition memory 213 , For each parameter of frame 1, the comparison block determines 212 the difference between the parameter value in the runtime matrix 174 and the parameter value in the note signature 2 and stores the difference in the recognition memory 213 , The differences between the parameters 1-j of the frame 1 and the parameters 1-j of the note signature 2 are summed to determine an overall difference value between the parameters 1-j of the frame 1 and the parameters 1-j of the note signature 2.

The time domain and frequency domain parameters 1-j in the runtime matrix 174 for frame 1, with the time domain and frequency domain parameters 1-in the remaining note signatures 3-i in the database 112 compared as described for the note signatures 1 and 2. The minimum total difference between the parameters 1-j of frame 1 of the runtime matrix 174 and the parameters 1-j of the note signatures 1-i is the best match or closest correlation. The frame 1 of the runtime matrix 174 is identified with the note signature with the minimum total difference between corresponding parameters. In this case, the time domain and frequency domain parameters are 1-j of frame 1 in the runtime matrix 174 more closely aligned with the time domain and frequency domain parameters 1-j in the note signature 2. The frame 1 of the runtime matrix 174 is identified as the frame of a beat style note.

With the time domain parameters and frequency domain parameters 1-j of frame 1 in the runtime matrix 174 that are generated by a played note that matches the note signature 2 uses the block 114 for adaptive intelligence control of 6 the control parameters 1-k in the database 112 associated with the appropriate note signature 2 for the operation of the signal processing blocks 92 - 104 of the audio amplifier 90 to control.

The audio signal is passed through the pre-filter block 92 , the pre-effect block 94 , the block 96 for non-linear effects, the user-defined modules 98 , the after effect block 100 , the postfilter block 102 and the power amplification block 104 processed, each of which, as determined by the control parameter 2.1, the control parameter 2.2 to the control parameter 2, k of the note signature 2. The improved audio signal becomes the speaker in the case 24 or to the speaker 82 in the case 72 directed. The listener hears the reproduced audio signal in real time with characteristics determined by the dynamic content of the audio signal.

The time domain and frequency domain parameters 1-j for frame 2 in the runtime matrix 174 and the parameters 1-j in all the note signatures 1-i are successively compared on a basis and the differences are recorded. For each parameter 1-j of frame 2, the comparison block determines 212 the difference between the parameter value in the runtime matrix 174 and the parameter value in the note signature i and stores the difference in the recognition memory 213 , The differences between the parameters 1-j of the frame 2 and the parameters 1-j of the note signature i are summed to determine a total difference value between the parameters 1-j of the frame 2 and the parameters 1-j of the note signature i. The minimum total difference between the parameters 1-j of frame 2 of the runtime matrix 174 and the parameters 1-j of the note signatures 1-i is the best match or closest correlation. The frame 2 of the runtime matrix 174 is identified with the note signature with the minimum total difference between corresponding parameters. In this case, the time domain and frequency domain parameters 1-j of frame 2 are in the runtime matrix 174 more closely aligned with the time domain and frequency domain parameters 1-j in the note signature 2. The frame 2 of the runtime matrix 174 is identified as another frame of a beat style note. The block 114 for adaptive intelligence control uses the control parameters 1-k in the database 112 associated with the appropriate note signature 2 for the operation of the signal processing blocks 92 - 104 of the audio amplifier 90 to control. The process continues for each frame n of the runtime matrix 174 continued.

In another embodiment, the time domain and frequency domain parameters 1-j for a frame in the runtime matrix 174 and the parameters 1-j in all note signatures 1-i are compared on a sequential basis and the weighted differences are recorded. The headset usage parameter of frame 1 in the runtime matrix 174 for example, has a value of 28 (see Table 2), and the note-point usage parameter in note signature 1 has a value of 30 (see Table 4). The comparison block 212 determines the weighted difference (30-28) x weight 1.1 and stores the weighted difference in the recognition memory 213 , The note peak enable parameter of frame 1 in the runtime matrix 174 has a value of 196 (see Table 2) and the note-point enable parameter in note signature 1 has a value of 200 (see Table 4). The comparison block 212 determines the weighted difference (200-196) x weight 1,2 and stores the weighted difference in the recognition memory 213 , For each parameter of frame 1, the comparison block determines 212 the weighted difference between the parameter value in the runtime matrix 174 and the parameter value in the note signature 1, as determined by the weight 1, j, and stores the weighted difference in the recognition memory 213 , The weighted differences between the parameters 1-j of the frame and the parameters 1-j of the note signature 1 are summed to determine a total weighted difference value between the parameters 1-j of the frame 1 and the parameters 1-j of the note signature 1.

Next, the note peak usage parameter of frame 1 in the runtime matrix 174 a value of 28 on (see Table 2) and the note-point use parameter in the note signature 2 has a value of 5 (see Table 5). The comparison block 212 determines the weighted difference (5-28) x weight 2,1 and stores the weighted difference in the recognition memory 213 , The note peak enable parameter of frame 1 in the runtime matrix 174 has a value of 196 (see Table 2) and the note-point enable parameter in note signature 2 has a value of 40 (see Table 5). The comparison block 212 determines the weighted difference (40-196) x weight 2,2 and stores the weighted difference in the recognition memory 213 , For each parameter of frame 1, the comparison block determines 212 the weighted difference between the parameter value in the runtime matrix 174 and the parameter value in the note signature 2 by the weight 2, j and stores the weighted difference in the recognition memory 213 , The weighted differences between the parameters 1-j of frame 1 in the runtime matrix 174 and the parameters 1-j of the note signature 2 are summed to determine a total weighted difference value between the parameters 1-j of the frame 1 and the parameters 1-j of the note signature 2.

The time domain and frequency domain parameters 1-j in the runtime matrix 174 for frame 1, with the time domain and frequency domain parameters 1-j in the remaining note signatures 3-i in the database 112 compared as described for the note signatures 1 and 2. The minimum weighted total difference between the parameters 1-j of frame 1 of the runtime matrix 174 and the parameters 1-j of the note signatures 1-i is the best match or closest correlation. The frame 1 of the runtime matrix 174 is identified with the note signature with the minimum weighted total difference between corresponding parameters. The block 114 for adaptive intelligence control uses the control parameters 1-k in the database 112 associated with the appropriate note signature to the operation of the signal processing blocks 92 - 104 of the audio amplifier 90 to control.

The time domain and frequency domain parameters 1-j for frame 2 in the runtime matrix 174 and the parameters 1-j in all the note signatures 1-i are successively compared on a basis and the weighted differences are recorded. For each parameter 1-j of frame 2, the comparison block determines 212 the weighted difference between the parameter value in the runtime matrix 174 and the parameter value in the note signature i by the weight i, j and stores the weighted difference in the recognition memory 213 , The weighted differences between the Parameters 1-j of the frame 2 and the parameters 1-j of the note signature i are summed to determine a total weighted difference value between the parameters 1-j of the frame 2 and the parameters 1-j of the note signature i. The minimum weighted total difference between the parameters 1-j of frame 2 of the runtime matrix 174 and the parameters 1-j of the note signatures 1-i is the best match or closest correlation. The frame 2 of the runtime matrix 174 is identified with the note signature with the minimum weighted total difference between corresponding parameters. The block 114 for adaptive intelligence control uses the control parameters 1-k in the database 112 associated with the appropriate note signature to the operation of the signal processing blocks 92 - 104 of the audio amplifier 90 to control. The process continues for each frame n of the runtime matrix 174 continued.

In a further embodiment, a probability for the correlation between corresponding parameters in the runtime matrix 174 and the note signatures 1-i. In other words, a probability of correlation is expressed as a percentage that is a given parameter in the runtime matrix 174 probably the same as the corresponding parameter in the note signature i. The percentage is a probability of a match. As described above, the time domain parameters and frequency domain parameters in the runtime matrix become 174 stored on a frame by frame basis. For every frame n of each parameter j in the runtime matrix 174 is represented by Pn, j = [Pn1, Pn2, ... Pnj].

A probability-ordered list R is placed between each frame n of each parameter j in the runtime matrix 174 and each parameter j of each note signature i. The probability value r _i can be determined by an analysis of the root-mean-squared value for the Pn, j and the note signature database Si, j in equation (3):

The probability value R is (1-r _i ) × 100%. The total rank order value for Pn, j and the score database S _{i, j} is given in Equation (4). R = [(1-r ₁ ) x 100% (1-r ₂ ) x 100% (1-r _i ) x 100%] (4)

In some cases, the comparison process identifies two or more note signatures that are close to the note being played. For example, the played note may have a probability of 52% matching the note signature 1 and a 48% chance that it matches the note signature 2. In this case, an interpolation between the control parameter 1,1, the control parameter 1,2 to the control parameter 1, k and the control parameter 2,1, the control parameter 2,2 to the control parameter 2, k, which is weighted by the probability of coincidence be performed. The net effective control parameter 1 is 0.52 · control parameters 1.1 + 0.48 · control parameters 2.1. The net effective control parameter 2 is 0.52 · control parameter 1.2 + 0.48 · control parameter 2.2. The net effective control parameter k is 0.52 · control parameters 1, k + 0.48 · control parameters 2, k. The net effective control parameters 1-k control the operation of the signal processing blocks 92 - 104 of the audio amplifier 90 , The audio signal is passed through the pre-filter block 92 , the pre-effect block 94 , the block 96 for non-linear effects, the user-defined modules 98 , the after effect block 100 , the postfilter block 102 and the power amplification block 104 each working as determined by the net effective control parameters 1-k. The audio signal becomes the speaker in the case 24 or to the speaker 82 in the case 72 directed. The listener hears the reproduced audio signal in real time with characteristics determined by the dynamic content of the audio signal.

In the 6 - 22 The adaptive intelligence control described above is applicable to other musical instruments that produce notes with a different deployment phase followed by a hold phase, decay phase, and release phase. The audio signal may, for example, from a stringed musical instrument, such as. A violin, a violin, a harp, a mandolin, a viola, a banjo, a cello, just to name a few. The audio signal may be from percussion instruments such. Drums, bells, glockenspiels, cymbals, a piano, a tambourine, a xylophone and the like. The audio signal is passed through the time domain analysis block 122 and the frequency domain analysis block 120 processed on a frame by frame basis to isolate the played note and determine its characteristics. Each frame analyzed in the time domain and frequency domain will be in the database with note signatures 1-i 112 to identify the type of note and to determine the appropriate control parameters 1-k. The Signal processing functions in the audio amplifier 90 are set in accordance with the control parameters 1-k of the appropriate note signature to reproduce the audio signal in real time with improved characteristics determined by the dynamic content of the audio signal.

The signal processing functions may be associated with equipment other than a dedicated audio amplifier. 23 shows a musical instrument 214 which generates an audio signal that goes with the equipment 215 is directed. Equipment 215 performs the signal processing functions on the audio signal. The signal-processed audio signal becomes the audio amplifier 216 for power amplification or attenuation of the audio signal. The audio signal then becomes the loudspeaker 217 directed to the sound content of the musical instrument 214 with the improvements made by the signal processing equipment 215 be introduced into the audio signal to play.

In one embodiment, the signal processing equipment is 215 a computer 218 , as in 24 shown. The computer 218 includes digital signal processing components and software for implementing the signal processing function. 25 is a block diagram of the signal processing function 220 that are inside the computer 218 including a pre-filter block 222 , a pre-effect block 224 , one block 226 for non-linear effects, user-defined modules 228 , a post-effect block 230 and a postfilter block 232 , The pre-filter block 222 and the postfilter block 232 provide different filter functions, such as B. low pass filtering and bandpass filtering of the audio signal. The prefiltering and post-filtering may include tone equalization functions over different frequency ranges to enhance or attenuate the levels of specific frequencies without affecting adjacent frequencies, such as: B. Bass frequency adjustment and Höhenfrequenzeinstellung. Tone equalization may include, for example, step equalization to amplify or attenuate all frequencies above or below a target or fundamental frequency, bell equalization to amplify or attenuate a narrow range of frequencies by a target or fundamental frequency, graphic equalization, or the like use a parameter equalization. The pre-effect block 224 and the after effect block 230 introduce sound effects into the audio signal, such as For example, reverb, delays, multiple overlays, wah, auto volume, phase shifter, humming suppressor, squelch, vibrato, pitch shifting, graphic equalization, tremolo, and dynamic compression. The block 226 for nonlinear effects, non-linear effects introduce into the audio signal, such as: M modeling, distortion, clipping, blur, and modulation. The block 228 The user-defined modules allow the user to define customized signal processing functions, such as: B. Addition of accompaniment instruments, vocals and synthesizer options. The audio signal after signal processing becomes the audio amplifier 216 and to the speaker 217 directed.

The pre-filter block 222 , the pre-effect block 224 , the block 226 for non-linear effects, the user-defined modules 228 , the after effect block 230 and the postfilter block 232 within the signal processing function are with the front panel 234 ie by the computer keyboard or an external control signal for the computer 218 , selectable and controllable.

To accommodate the signal processing requirements for the dynamic content of the audio source, the computer uses 218 a dynamic adaptive intelligence feature that includes frequency domain analysis and time domain analysis of the audio signal on a frame by frame basis and automatically and adaptively controls the operation of the signal processing functions and settings within the computer to achieve optimal sound reproduction. The audio signal from the musical instrument 214 becomes the frequency domain and time domain analysis block 240 directed. The output of the block 240 becomes the note signature block 242 passed and the output of the block 242 becomes the block 244 directed to adaptive intelligence control.

The functions of the blocks 240 . 242 and 244 correspond to the blocks 110 . 112 respectively. 114 , as in 6 - 19 described. The blocks 240 - 244 perform real-time frequency domain analysis and time domain analysis of the audio signal on a frame by frame basis. Each incoming frame of the audio signal is detected and analyzed to determine its time domain and frequency domain content and its time domain and frequency domain characteristics. The incoming frame is compared to a database of fixed or learned note signatures to determine a best match or closest correlation of the incoming frame with the database of note signatures. The most appropriate note signature from the database contains the control configuration of the signal processing blocks 222 - 232 , The most appropriate note signature controls the operation of the signal processing blocks 222 - 232 in real time on a frame by frame basis to continuously and automatically adjust the signal processing functions for optimal sound reproduction, as in 6 - 19 described. For example, based on the control parameter 1-k of the appropriate note signature, the gain of the audio signal can be automatically increased or decreased for that particular note. Presets and sound effects can be automatically turned on or removed for the note being played. The next frame in the sequence may be associated with the same note that matches the same note signature in the database, or the next frame in the sequence may be associated with a different note that matches another corresponding note signature in the database. Each frame is recognized and compared to a note signature containing control parameters 1-k to the operation of the signal processing blocks 222 - 232 within the audio amplifier 220 to control for optimal sound reproduction. The signal processing blocks 222 - 232 are set according to the most appropriate note signature corresponding to each individual incoming frame to improve their reproduction.

26 shows another embodiment of the signal processing equipment 215 as a pedal keyboard or sound machine 246 , The pedal keyboard 246 contains signal processing blocks, as for 25 and with reference to 6 - 22 described. The pedal keyboard 246 employs a dynamic adaptive intelligence feature that includes frequency domain analysis and time domain analysis of the audio signal on a frame by frame basis and automatically and adaptively controls the operation of the signal processing functions and settings within the pedal keyboard for optimum sound reproduction. Each incoming frame of the audio signal is detected and analyzed to determine its time domain and frequency domain content and its time domain and frequency domain characteristics. The incoming frame is compared to a database of fixed or learned note signatures to determine a best match or closest correlation of the incoming frame with the database of note signatures. The most suitable note signature contains control parameters 1-k that control the operation of the signal processing blocks in real time on a frame-by-frame basis to continuously and automatically adjust the signal processing functions for optimal sound reproduction.

27 shows another embodiment of the signal processing equipment 215 as signal processing housing 248 , The signal processing housing 248 contains signal processing blocks, as for 25 and with reference to 6 - 22 described. The signal processing housing 248 uses a dynamic adaptive intelligence feature that includes frequency domain analysis and time domain analysis of the audio signal on a frame-by-frame basis and automatically and adaptively controls the operation of the signal processing functions and settings within the signal processing housing to achieve optimal sound reproduction. Each incoming frame of the audio signal is detected and analyzed to determine its time domain and frequency domain content and its time domain and frequency domain characteristics. The incoming frame is compared to a database of fixed or learned note signatures to determine a best match or closest correlation of the incoming frame with the database of note signatures. The most suitable note signature contains control parameters 1-k that control the operation of the signal processing blocks in real time on a frame-by-frame basis to continuously and automatically adjust the signal processing functions for optimal sound reproduction.

Some embodiments of the audio source 12 are better characterized on a frame by frame basis, ie no clear or reliably detectable demarcation between the notes. For example, the audio signal of voice samples may be better suited for frame-by-frame analysis without detecting the beginning of a note. 28 shows the audio source 12 as a microphone 250 presented by a man or woman with vocal ranges including soprano, mezzo soprano, alto, tenor, baritone and bass. The microphone 250 is through an audio cable 252 with an audio system with an audio amplifier inside a first case 254 is included, and a speaker inside a second separate housing 256 recorded, connected. The audio cable 252 from the microphone 250 becomes the audio input jack 258 which are connected to the audio amplifier inside the case 254 for power amplification and signal processing. control buttons 260 at the front panel 262 of the housing 254 allow the user to monitor and manually control various settings of the audio amplifier. The housing 254 is through an audio cable 264 with the housing 256 electrically connected to the amplified and conditioned audio signal to the speakers 266 to lead.

29 is a block diagram of the audio amplifier 270 inside the case 254 is included. The audio amplifier 270 receives audio signals from the microphone 250 through the audio cable 252 , The audio amplifier 270 performs a gain and other signal processing functions, such as. B. equalization, filtering, sound effects and user-defined modules, the audio signal through to adjust the power level and otherwise improve the signal characteristics for the listening experience.

The audio amplifier 270 has a signal processing path for the audio signal including a pre-filter block 272 , a pre-effect block 274 , a block 276 for non-linear effects, user-defined modules 278 , a post-effect block 280 , a postfilter block 282 and a power amplification block 284 , The pre-filter block 272 and the postfilter block 282 see different filter functions such. B. low pass filtering and bandpass filtering of the audio signal before. The prefiltering and postfiltering may include tone equalization functions over various frequency ranges to enhance or attenuate the levels of specific frequencies without affecting adjacent frequencies, such as, for example: B. Bass frequency adjustment and Höhenfrequenzeinstellung. Tone equalization may include, for example, step equalization to amplify or attenuate all frequencies above or below a target or fundamental frequency, bell equalization to amplify or attenuate a narrow range of frequencies by a target or fundamental frequency, graphic equalization, or the like use a parameter equalization. The pre-effect block 274 and the after effect block 280 introduce sound effects into the audio signal, such as For example, reverb, delays, multiple overlays, wah, auto volume, phase shifter, humming suppressor, squelch, vibrato, pitch shifting, graphic equalization, tremolo, and dynamic compression. The block 276 for nonlinear effects, non-linear effects introduce into the audio signal, such as: M modeling, distortion, clipping, blur, and modulation. The block 278 for user defined modules allows the user to define custom signal processing functions, such as: B. Addition of accompaniment instruments, vocals and synthesizer options. The power amplifier block 284 provides power amplification or attenuation of the audio signal. The audio signal after signal processing becomes the speakers 266 in the case 256 directed.

The pre-filter block 272 , the pre-effect block 274 , the block 276 for non-linear effects, the user-defined modules 278 , the after effect block 280 , the post filter block 282 and the power amplification block 284 within the audio amplifier 270 are with the front panel 262 selectable and controllable. By turning the buttons 260 at the front panel 262 the user can control the operation of the signal processing functions within the audio amplifier 270 direct control.

29 further provides the dynamic adaptive intelligence control of the audio amplifier 270 A main purpose of the adaptive intelligence feature of the audio amplifier 270 consists in detecting and isolating the frequency domain characteristics and time domain characteristics of the audio signal on a frame by frame basis and using this information to control the operation of the signal processing blocks 272 - 284 to control the amplifier. The audio signal from the audio cable 252 becomes the frequency domain and time domain analysis block 290 directed. The output of the block 290 becomes the frame signature block 292 passed and the output of the block 292 becomes the block 294 directed to adaptive intelligence control. The functions of the blocks 290 . 292 and 294 are discussed in turn.

30 represents another detail of the frequency domain and time domain analysis block 290 including the audio sample block 296 , the frequency domain analysis block 300 and the time domain analysis block 302 , The analog audio signal is sent to the audio sampling block 296 to hand over. The audio sampling block 296 samples the analog audio signal, eg. From 512 to 1024 samples per second, using an A / D converter. The sampled audio signal 298 is divided into a series of temporally progressive frames (frame 1 to frame n) each containing a predetermined number of samples of the audio signal. 31a shows the frame 1, the 1024 samples of the audio signal 298 in the time sequence, the frame 2 containing the next 1024 samples of the audio signal 298 in the time sequence, the frame 3 containing the next 1024 samples of the audio signal 298 in the time sequence, and so on until frame n, contains 1024 samples of the audio signal 298 in the time sequence contains. 31b shows overlapping windows 299 of frames 1-n used in the time domain frequency domain conversion as in 34 described. The sampled audio signal 298 becomes the frequency domain analysis block 300 and time domain analysis block 302 directed.

32 represents another detail of the time domain analysis block 302 with the energy level insulation block 304 representing the energy level of each frame of the sampled audio signal 298 isolated in several frequency bands. In 33 processes the energy level isolation block 304 every frame of the sampled audio signal 298 in the time sequence through the filter frequency band 310a - 310c to separate and isolate specific frequencies of the audio signal. The filter frequency bands 310a - 310c can isolate specific frequency bands in the audio range of 100-10000 Hz. In one embodiment, the filter frequency band is 310a a bandpass filter having a passband centered at 100 Hz, the filter frequency band 310b is a bandpass filter with a passband centered at 500 Hz and the filter frequency band 310c is a bandpass filter with a passband centered at 1000 Hz. The output of the filter frequency band 310a contains the energy level of the sampled audio signal 298 centering at 100 Hz is. The output of the filter frequency band 310b contains the energy level of the sampled audio signal 298 which is centered at 500 Hz. The output of the filter frequency band 310c contains the energy level of the sampled audio signal 298 which is centered at 1000 Hz. The output of other filter frequency bands each contains the energy level of the sampled audio signal 298 for a given specific band. A top detector 312a monitors and stores peak energy levels of the sampled audio signal 298 that are centered at 100 Hz. A top detector 312b monitors and stores the peak energy levels of the sampled audio signal 298 that are centered at 500 Hz. A top detector 312c monitors and stores the peak energy levels of the sampled audio signal 298 that are centered at 1000 Hz. A smoothing filter 314a removes interfering components and otherwise stabilizes the peak energy levels of the sampled audio signal 298 that are centered at 100 Hz. A smoothing filter 314b removes interfering components and otherwise stabilizes the peak energy levels of the sampled audio signal 298 that are centered at 500 Hz. A smoothing filter 314c Removes interfering components of the peak power levels and otherwise stabilizes the sampled audio signal 298 that are centered at 1000 Hz. The output of the smoothing filters 314a - 314c For example, the energy level function E (m, n) for each frame n in each frequency band is 1-m of the sampled audio signal 298 ,

A time domain analysis block 302 from 26 also includes a transition detector block 322 , as in 32 shown. The block 322 uses the energy function E (m, n) to track rapid or significant transient changes in energy levels over time, indicating a change in sound content. The transition detector is a time domain parameter or time domain characteristic of each frame n for all frequency bands 1-m and is considered to be the value in the runtime matrix 324 stored on a frame by frame basis.

A vibrato detector block 326 uses the energy function E (m, n) to track changes in the amplitude of the energy levels over time, indicating an amplitude modulation associated with the vibrato effect. The vibrato detector is a time domain parameter or time domain characteristic of each frame n for all frequency bands 1-m and is considered to be the value in the runtime matrix 324 stored on a frame by frame basis.

The frequency domain analysis block 300 in 26 includes an STFT block 338 , as in 34 shown. The block 338 performs a time domain frequency domain conversion on a frame by frame basis of the sampled audio signal 118 using a COLA-STFT or another FFT. The COLA-STFT 338 performs a time domain frequency domain conversion using overlap analysis windows 299 through, as in 31b shown. The sampling windows 299 overlap by a predetermined number of samples of the audio signal, known as a hop size, for additional sample points in the COLA-STFT analysis to ensure that the data is weighted equally in successive frames. Equation (2) provides a general format of the time domain frequency domain conversion on the sampled audio signal 298 ,

Once the sampled audio signal 298 is in the frequency domain, uses a vowel "a" formatter block 340 the sampled frequency domain audio signal to indicate the occurrence of the vowel "a" in the sampled audio signal 298 to determine. Each vowel has a frequency setting. The vowel "a" occurs in the range of 800-1200 Hz and in no other frequency range. The vowel "a" form parameter is the value one when the vowel is in the sampled audio signal 298 exists, and the value is null if the vowel does not exist. The vowel "a" form is a frequency domain parameter or frequency domain characteristic of each frame n and is considered to be a value in the runtime matrix 324 stored on a frame by frame basis.

A vowel "e" formatter block 342 uses the sampled frequency domain audio signal to detect the occurrence of the vowel "e" in the sampled audio signal 298 to determine. The vowel "e" occurs in the range of 400-600 Hz and also in the frequency range of 2200-2600. The vowel "e" form parameter is the value one when the vowel is in the sampled audio signal 298 exists, and the value is null if the vowel does not exist. The vowel "e" form is a frequency domain parameter or frequency domain characteristic of each frame n and is considered to be a value in the runtime matrix 324 stored on a frame by frame basis.

A vocal "i" formatter block 344 uses the sampled frequency domain audio signal to detect the occurrence of the vowel "i" in the sampled audio signal 298 to determine. The vowel "i" occurs in the range of 200-400 Hz and also in the frequency range of 3000-3500. The vowel "i" form parameter is the value one when the vowel is in the sampled audio signal 298 exists, and the value is null if the vowel is not is available. The vowel "i" form is a frequency domain parameter or frequency domain characteristic of each frame n and is considered to be a value in the runtime matrix 324 stored on a frame by frame basis.

A vowel "o" formatter block 346 uses the sampled frequency domain audio signal to detect an occurrence of the vocal "o" sampled audio signal 298 to determine. The vowel "o" occurs in the range of 400-600 Hz and in no other frequency range. The vowel "o" form parameter is the value one when the vowel is in the sampled audio signal 298 exists, and the value is null if the vowel does not exist. The vowel "o" form is a frequency domain parameter or frequency domain characteristic of each frame n and is considered to be a value in the runtime matrix 324 stored on a frame by frame basis.

A vowel "u" formatter block 348 uses the sampled frequency domain audio signal to detect the occurrence of the vowel "u" in the sampled audio signal 298 to determine. The vowel "u" occurs in the range of 200-400 Hz and in no other frequency range. The vowel "u" form parameter is the value one when the vowel is in the sampled audio signal 298 exists, and the value is null if the vowel does not exist. The vowel "u" form is a frequency domain parameter or frequency domain characteristic of each frame n and is considered to be a value in the runtime matrix 324 stored on a frame by frame basis.

An overtone detector block 350 uses the sampled frequency domain audio signal to detect a higher harmonic resonance or an overtone of the fundamental, giving the impression of simultaneous sounds. The overtone detector is a frequency domain parameter or frequency domain characteristic of each frame n and is considered to be a value in the runtime matrix 324 stored on a frame by frame basis.

The runtime matrix 324 contains the time range parameters that are in the time domain analysis block 302 be determined, and the frequency domain parameters included in the frequency domain analysis block 300 be determined. Each time domain parameter and frequency domain parameter is a numerical parameter value PVn, j, which is in the runtime matrix 324 is stored on a frame by frame, where n is the frame and j is the parameter, similar to Table 1. The time domain and frequency domain parameter values Pn, j are characteristic of specific frames and therefore useful in distinguishing between frames.

With return to 29 becomes the database 292 in a memory component of the audio amplifier 270 and contains a plurality of frame signature records 1, 2, 3, ... i, each frame signature having time domain parameters and frequency domain parameters corresponding to the runtime matrix 324 correspond. In addition, the frame signature records 1-i include weighting factors 1, 2, 3, ... j for each time domain and frequency domain parameter and a plurality of control parameters 1, 2, 3, ... k.

35 shows the database 292 with time domain and frequency domain parameters 1-j for each frame signature 1-i, weighting factors 1-j for each frame signature 1-i and control parameters 1-k for each frame signature 1-i. Each frame signature record i is defined by the parameters 1-j and associated weights 1-j characteristic of the frame signature, and is used to form an incoming frame from the runtime matrix 324 to identify with the frame signature i as best matching or closest correlated. Once the incoming frame from the runtime matrix 324 is matched with a special frame signature i, uses adaptive intelligence control 294 the control parameters 1-k for the appropriate frame signature, the operating state of the signal processing blocks 272 - 284 of the audio amplifier 270 adjust. In a suitable frame signature data set i, for example, the control parameter i, 1 sets the operating state of the pre-filter block 272 firmly; the control parameter i, 2 defines the operating state of the pre-effect block 274 firmly; the control parameter i, 3 sets the operating state of the block 276 for non-linear effects; the control parameter i, 4 defines the operating state of the modules defined by the user 278 firmly; the control parameter i, 5 defines the operating state of the after-effect block 280 firmly; the control parameter i, 6 sets the operating state of the postfilter block 282 firmly; and the control parameter i, 7 sets the operating state of the power amplification block 284 firmly.

The time domain parameters and frequency domain parameters in the frame signature database 292 contain values specified by the manufacturer or entered by the user or learned over time by playing an instrument. The factory or the manufacturer of the audio amplifier 270 may initially set the values of the time domain and frequency domain parameters 1-j and weighting factors 1-j and control parameters 1-k. The user can change the time range and Frequency domain parameters 1-j, the weighting factors 1-j and the control parameters 1-k for each frame signature 1-i in the database 292 directly using the computer 352 with the user interface screen or user interface display 354 , please refer 36 , to change. The values for the time domain and frequency domain parameters 1-j, the weighting factors 1-j and the control parameters 1-k are displayed on the interface screen 354 to allow the user to enter updated values.

In another embodiment, time domain and frequency domain parameters 1-j, weighting factors 1-j, and control parameters 1-k may be input by the artist into the microphone 250 sings, be learned. The artist puts the audio amplifier 270 into a learning mode. The artist sings repeatedly in the microphone 250 , The frequency domain analysis 300 and the time domain analysis 302 from 30 generate a runtime matrix 324 with associated frequency domain parameters and time domain parameters for each frame 1-n, as in 31a Are defined. The frequency domain parameters and time domain parameters for each frame 1-n are in the database 292 collected and saved.

The artist can manual settings on the audio amplifier 270 via the front panel 262 carry out. The audio amplifier 270 learns the control parameters 1-k associated with the frame through the settings of the signal processing blocks 272 - 284 as manually set by the artist. When the learn mode is completed, the frame signature records will be in the database 292 defined with the frame signature parameters, which are an average of the frequency domain parameters and the time domain parameters stored in the database 292 are collected, and an average of the control parameters 1-k, that of the manual settings of the signal processing blocks 272 - 284 of the audio amplifier 270 in the database 292 are taken. In one embodiment, the mean is a root mean square of the series of collected frequency domain and time domain parameters 1-j and collected control parameters 1-k in the database 292 ,

Weighting factors 1-j may be learned by monitoring the learned time domain and frequency domain parameters 1-j and increasing or decreasing the weighting factors based on the proximity or statistical correlation of the comparison. If a particular parameter has a consistent statistical correlation, then the weighting factor for that parameter can be increased. If a particular parameter has a different statistical miscellaneous correlation then the weighting factor for that parameter can be reduced.

Once the parameters 1-j, the weighting factors 1-j and the control parameters 1-k of the frame signatures 1-i for the database 292 are fixed, the time domain and frequency domain parameters 1-j in the runtime matrix 324 are compared on a frame-by-frame basis with each frame signature 1-i to find a best match or closest correlation. In a normal playback mode, the artist sings a lyrics text to produce an audio signal with a time sequence of frames. For each frame, the runtime matrix becomes 324 is occupied by time domain parameters and frequency domain parameters determined from a time domain analysis and frequency domain analysis of the audio signal, as in 29 - 34 described.

The time domain and frequency domain parameters 1-j for frame 1 in the runtime matrix 324 and the parameters 1-j in each frame signature 1-i are successively compared on a basis and the differences are recorded. 37 shows a recognition detector 356 with a comparison block 358 for determining the difference between the time domain and frequency domain parameters 1-j for a frame in the runtime matrix 324 and the parameters 1-j in each frame signature 1-i. For each parameter of frame 1, the comparison block determines 358 the difference between the parameter value in the runtime matrix 324 and the parameter value in the frame signature 1 and stores the difference in the recognition memory 360 , The differences between the parameters 1-j of frame 1 in the runtime matrix 324 and the parameters 1-j of the frame signature 1 are summed to determine an overall difference value between the parameters 1-j of the frame 1 and the parameters 1-j of the frame signature 1.

Next, the comparison block determines 358 for each parameter of frame 1, the difference between the parameter value in the runtime matrix 324 and the parameter value in the frame signature 2, and stores the difference in the recognition memory 360 , The differences between the parameters 1-j of frame 1 in the runtime matrix 324 and the parameters 1-j of the frame signature 2 are summed to determine an overall difference value between the parameters 1-j of the frame 1 and the parameters 1-j of the frame signature 2.

The time domain parameters and frequency domain parameters 1-j in the runtime matrix 324 for frame 1, with the time domain and frequency domain parameters 1-j in the remaining frame signatures 3-i in the database 292 compared as described for the frame signatures 1 and 2. The minimum total difference between the parameters 1-j of frame 1 of the runtime matrix 324 and the parameters 1- j of the frame signatures 1-i is the best match or closest correlation and the frame that corresponds to frame 1 of the runtime matrix 324 is associated with the frame signature with the minimum total difference between corresponding parameters. In this case, the time domain and frequency domain parameters 1-j of frame 1 are in the runtime matrix 324 more closely aligned to the time domain and frequency domain parameters 1-j in the frame signature 1.

With the time domain parameters and frequency domain parameters 1-j of frame 1 in the runtime matrix 324 that match the frame signature 1 uses the block 294 for adaptive intelligence control of 29 the control parameters 1-k, the matching frame signature 1 in the database 292 are assigned to the operation of the signal processing blocks 272 - 284 of the audio amplifier 270 to control. The audio signal is passed through the pre-filter block 272 , the pre-effect block 274 , the block 276 for non-linear effects, the user-defined modules 278 , the after effect block 280 , the postfilter block 282 and the power amplification block 284 processed, each of which, as determined by the control parameter 1,1, the control parameter 1,2 to the control parameter 1, k of the frame signature 1. The improved audio signal becomes the speaker 266 in the case 256 directed. The listener hears the reproduced audio signal in real time with characteristics determined by the dynamic content of the audio signal.

The time domain and frequency domain parameters 1-j for frame 2 in the runtime matrix 324 and the parameters 1-j in each frame signature 1-i are successively compared on a basis and the differences are recorded. For each parameter 1-j of frame 2, the comparison block determines 358 the difference between the parameter value in the runtime matrix 324 and the parameter value in the frame signature i and stores the difference in the recognition memory 360 , The differences between the parameters 1-j of frame 2 in the runtime matrix 324 and the parameters 1-j of the frame signature i are summed to determine a total difference value between the parameters 1-j of the frame 2 and the parameters 1-j of the frame signature i. The minimum total difference between the parameters 1-j of frame 2 of the runtime matrix 324 and the parameters 1-j of the frame signatures 1-i is the best match or closest correlation and the frame corresponding to frame 1 of the runtime matrix 324 is associated with the frame signature with the minimum total difference between corresponding parameters. In this case, the time domain and frequency domain parameters 1-j of frame 2 are in the runtime matrix 324 more closely aligned with the time domain and frequency domain parameters 1-j in the frame signature 2. The block 294 for adaptive intelligence control, the control parameter 1-k uses the appropriate frame signature 2 in the database 292 are assigned to the operation of the signal processing blocks 272 - 284 of the audio amplifier 270 to control. The process continues for each frame n of the runtime matrix 324 continued.

In another embodiment, the time domain and frequency domain parameters 1-j for a frame in the runtime matrix 324 and the parameters 1-j in each frame signature 1-i are successively compared on a basis and the weighted differences are recorded. For each parameter of frame 1, the comparison block determines 358 the weighted difference between the parameter value in the runtime matrix 324 and the parameter value in the frame signature 1, as determined by the weight 1, j, and stores the weighted difference in the recognition memory 360 , The weighted differences between the parameters 1-j of frame 1 in the runtime matrix 324 and the parameters 1-j of the frame signature 1 are summed to determine a total weighted difference value between the parameters 1-j of the frame 1 and the parameters 1-j of the frame signature 1.

Next, the comparison block determines 358 for each parameter of frame 1, the weighted difference between the parameter value in the runtime matrix 324 and the parameter value in the frame signature 2 by the weight 2, j and stores the weighted difference in the recognition memory 360 , The weighted differences between the parameters 1-j of the frame 1 and the parameters 1-j of the frame signature 2 are summed to determine a total weighted difference value between the parameters 1-j of the frame 1 and the parameters 1-j of the frame signature 2.

The time domain parameters and frequency domain parameters 1-j in the runtime matrix 324 for frame 1, with the time domain and frequency domain parameters 1-j in the remaining frame signatures 3-i in the database 292 compared as described for the frame signatures 1 and 2. The minimum weighted total difference between the parameters 1-j of frame 1 in the runtime matrix 324 and the parameters 1-j of the frame signatures 1-i is the best match or closest correlation and the frame corresponding to frame 1 of the runtime matrix 324 is associated with the frame signature with the minimum weighted total difference between corresponding parameters. The block 294 for controlling the adaptive intelligence uses the control parameters 1-k in the database 292 associated with the appropriate frame signature for the operation of the signal processing blocks 272 - 284 of the audio amplifier 270 to control.

The time domain and frequency domain parameters 1-j for frame 2 in the runtime matrix 324 and the parameters 1-j in each frame signature 1-i are successively compared on a basis and the weighted differences are recorded. For each parameter 1-j of frame 2, the comparison block determines 358 the weighted difference between the parameter value in the runtime matrix 324 and the parameter value in the frame signature i by the weight i, j and stores the weighted difference in the recognition memory 360 , The weighted differences between the parameters 1-j of frame 2 in the runtime matrix 324 and the parameters 1-j of the frame signature i are summed to determine a total weighted difference value between the parameters 1-j of the frame 2 and the parameters 1-j of the frame signature i. The minimum weighted total difference between the parameters 1-j of frame 2 of the runtime matrix 324 and the parameters 1-j of the frame signatures 1-i is the best match or closest correlation and the frame corresponding to frame 1 of the runtime matrix 324 is associated with the frame signature with the minimum weighted total difference between corresponding parameters. The block 294 for adaptive intelligence control uses the control parameters 1-k in the database 292 associated with the appropriate frame signature for the operation of the signal processing blocks 272 - 284 of the audio amplifier 270 to control. The process continues for each frame n of the runtime matrix 324 continued.

In a further embodiment, a probability for the correlation between corresponding parameters in the runtime matrix 324 and the frame signatures 1-i. In other words, a probability of correlation is expressed as a percentage that is a given parameter in the runtime matrix 324 probably the same as the corresponding parameter in the frame signature i. The percentage is a probability of a match. As described above, the time domain parameters and frequency domain parameters in the runtime matrix become 324 stored on a frame by frame basis. For every frame n of each parameter j in the runtime matrix 174 is represented by Pn, j = [Pn1, Pn2, ... Pnj].

A probability-ordered list R is placed between each frame n of each parameter j in the runtime matrix 174 and each parameter j of each frame signature i. The probability value r _i can be determined by an analysis of the root-mean-squared value for the Pn, j and the frame signature database Si, j in equation (3). The probability value R is (1-r _i ) × 100%. The total rank order value for Pn, j and the frame database Si, j is given in Equation (4).

In some cases, the comparison process identifies two or more frame signatures that are close to the present frame. A frame in the runtime matrix 324 For example, a probability of 52% that it matches the frame signature 1 and a probability of 48% that it matches the frame signature 2 may have. In this case, an interpolation between the control parameter 1,1, the control parameter 1,2 to the control parameter 1, k and the control parameter 2,1, the control parameter 2,2 to the control parameter 2, k, which is weighted by the probability of coincidence be performed. The net effective control parameter 1 is 0.52 · control parameters 1.1 + 0.48 · control parameters 2.1. The net effective control parameter 2 is 0.52 · control parameter 1.2 + 0.48 · control parameter 2.2. The net effective control parameter k is 0.52 · control parameters 1, k + 0.48 · control parameters 2, k. The net effective control parameters 1-k control the operation of the signal processing blocks 272 - 284 of the audio amplifier 270 , The audio signal is passed through the pre-filter block 272 , the pre-effect block 274 , the block 276 for non-linear effects, the user-defined modules 278 , the after effect block 280 , the postfilter block 282 and the power amplification block 284 each working as determined by the net effective control parameters 1-k. The audio signal becomes the speaker 266 in the case 256 directed. The listener hears the reproduced audio signal in real time with characteristics determined by the dynamic content of the audio signal.

Although one or more embodiments of the present invention have been shown in detail, those skilled in the art will recognize that modifications and adaptations may be made to these embodiments without departing from the scope of the present invention as set forth in the following claims.

Claims (17)

An audio system comprising a signal processor coupled to receive an audio signal, wherein the dynamic content of the audio signal controls the operation of the signal processor. The audio system of claim 1, further comprising: a time domain processor coupled to receive the audio signal and to generate time domain parameters of the audio signal; a frequency domain processor coupled to receive the audio signal and to generate frequency domain parameters of the audio signal; a signature database having a plurality of signature records each having time domain parameters and frequency domain parameters and control parameters; and a recognition detector for comparing the time domain parameters and frequency domain parameters of the audio signal with a signature data set of the signature database, the control parameters of the matching signature data set controlling the operation of the signal processor. The audio system of claim 1, wherein the signal processor comprises a pre-filter, pre-effects, non-linear effects, a user-defined module, post-effects, a post-filter or a power gain. The audio system of claim 1, wherein the audio signal is sampled and the time domain processor and the frequency domain processor process a plurality of frames of the sampled audio signal. The audio system of claim 1, wherein the time domain processor or the frequency domain processor detects the beginning of a note of the sampled audio signal. The audio system of claim 1, wherein the time domain parameters include a headset parameter, a note head enable parameter, a multiband peak set parameter, a multiband peak enable parameter, a beat detector parameter, a tempo detector parameter, a transition detector parameter, or a vibrato detector parameter. The audio system of claim 1, wherein the frequency domain parameters include a harmonic insertion ratio parameter, a harmonic release ratio parameter, an empty and attenuation factor parameter, a neck and bridge factor parameter, a pitch detector parameter, a vocal deformant parameter, or an overtone detector parameter. The audio system of claim 1, wherein the audio signal is generated by a guitar. The audio system of claim 1, wherein the audio signal is generated by vocals. A method of controlling an audio system, comprising: Providing a signal processor adapted to receive an audio signal; and Controlling the operation of the signal processor using the dynamic content of the audio signal. The method of claim 10, further comprising: Generating time domain parameters of the audio signal; Generating frequency domain parameters of the audio signal; Providing a signature database having a plurality of signature records each having time domain parameters and frequency domain parameters and control parameters; Comparing the time domain parameters and frequency domain parameters of the audio signal with a signature data set of the signature database; and Controlling the operation of the signal processor based on the control parameters of the matching signature data set. The method of claim 10, wherein the signal processor comprises a pre-filter, pre-effects, non-linear effects, a user-defined module, post-effects, a post-filter or a power gain. The method of claim 10, further comprising: Sampling the audio signal; and Generating the time domain parameters and frequency domain parameters based on a plurality of frames of the sampled audio signal. The method of claim 10, further comprising detecting a beginning of a note of the sampled audio signal. The method of claim 10, wherein the time domain parameters include a headset parameter, a note head enable parameter, a multiband peak-set parameter, a multiband peak-enable parameter, a beat detector parameter, a tempo detector parameter, a transition detector parameter, or a vibrato-detector parameter. The method of claim 10, wherein the frequency domain parameters include a harmonic duty cycle parameter, a harmonic clearance ratio parameter, an empty and attenuation factor parameter, a throat and bridge factor parameter, a pitch detector parameter, a vocal deformant parameter, or an overtone detector parameter. The method of claim 10, further comprising generating the audio signal with a guitar or vocals.

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